Group T2 - E
Organizations increasingly rely on data-driven insights to understand the factors that shape employee performance, retention, and career progression. With the growing adoption of flexible work arrangements, particularly work-from-home policies, managers face important strategic questions: How do these arrangements affect productivity? Do they influence employee turnover? And what role do they play in promotions or role changes?
This project aims to analyze the relationship between work arrangements and key employee outcomes, namely productivity, attrition, and promotion. Using regression-based modeling, the analysis evaluates how remote work and other employee characteristics contribute to variations in these outcomes. The objective is not only to identify statistically significant relationships, but also to interpret their magnitude and practical relevance in clear business terms.
To achieve this, the datasets are first validated and merged to ensure consistency and readiness for modeling. A set of dependent variables capturing employee productivity, attrition decisions, and promotion outcomes is then defined, alongside relevant explanatory variables such as work-from-home indicators and individual characteristics. Depending on the nature of each outcome, appropriate regression techniques are applied, including linear models for continuous measures and binary outcome models for turnover and promotion events.
By translating empirical findings into actionable insights, this analysis seeks to support management in designing effective workforce policies. In particular, it provides evidence-based recommendations regarding remote work practices, retention strategies, and employee development initiatives, helping organizations better align human resource decisions with performance and long-term organizational goals.
This technical documentation outlines the complete process of data preparation and regression analysis, which consists of two major stages: ensuring data readiness and conducting statistical modeling.
The first stage focuses on validating variable types, resolving any remaining inconsistencies, performing minimal exploratory checks, and merging all datasets into a unified analysis file.
The second stage involves selecting key outcome variables, defining appropriate predictors, applying suitable regression methods, and interpreting results in clear business terms to produce actionable, evidence-based recommendations for management.
The analysis draws upon five primary cleaned datasets:
attitude_clean — Weekly employee attitude survey
dataendperiod_outcomes_clean — End-of-period job outcomes
such as promotions and job exitsperformance_clean — Weekly job performance indicators
and productivity metricswage_clean — Monthly wage and compensation
recordssummary_clean — Individual demographic and household
informationEach dataset undergoes a data validation phase to ensure that all variables are correctly typed, consistently formatted, and free of remaining missing or inconsistent values. Instead of deep cleaning, the focus is on confirming data integrity and preparing the datasets for modeling. After validation, the datasets are merged using consistent key variables to create a comprehensive analytical file suitable for regression analysis.
A brief exploratory data assessment is conducted to confirm that the merged dataset is structurally sound and ready for statistical modeling. This includes summary statistics, distribution checks for key variables, and documentation of any transformations applied during preparation.
The second stage consists of the regression analysis, where selected dependent variables (e.g., productivity, attrition, promotion likelihood) are modeled using appropriate regression techniques such as linear regression (OLS) for continuous outcomes and logistic or probit models for binary outcomes. Relevant predictors include work arrangement indicators and employee characteristics.
This documentation provides a clear explanation of each step, including the R code, the methods applied, and the reasoning behind each decision. The objective is to ensure transparency, reproducibility, and analytical rigor throughout the entire data preparation and regression analysis workflow.
In the appendix at the end of this document, panel data regressions will also be included** as a bonus after the main analysis as confirmed in class and via e-mail with the course instructor Miguel.
1 - Introduction
2 - Data Cleaning Validation and Preparation
3 - Data Merging
4 - Minimal Exploratory Data Analysis (EDA)
5 - Regression Analysis and Interpretation
6 - Appendix with Panel Data Regressions (Bonus)
We begin by clearing the R environment to avoid
conflicts with previous objects.
Then, we load all the required packages:
R Packages Used
tidyverse, dplyr, tidyr,
readr – core data wrangling and manipulationjanitor – data cleaning and integrity checksskimr – summary statistics and quick data
diagnosticshaven – importing Stata .dta fileslubridate – date and time handlingstringr – string and text processingggplot2, ggthemes, GGally,
patchwork, scales – visualization and plotting
toolscorrr – correlation matrices and correlation
analysisinfer – inferential statistics and resampling-based
testingstargazer – regression tables and model output
formattingAdditional Regression & Diagnostic Packages
car – regression diagnostics, VIF, hypothesis
testsmargins – marginal effects for regression modelspscl – pseudo R² and advanced evaluation for
Logit/Probit modelssandwich – robust and cluster-robust standard
errorslmtest – hypothesis testing for linear and generalized
linear models# Clear the environment
rm(list = ls())
packages <- c(
"tidyverse",
"dplyr",
"tidyr",
"readr",
"janitor",
"skimr",
"haven",
"lubridate",
"stringr",
"ggplot2",
"ggthemes",
"GGally",
"patchwork",
"scales",
"corrr",
"infer",
"stargazer",
"car",
"margins",
"pscl",
"sandwich",
"lmtest",
"corrr",
"magrittr",
"plm",
"mfx"
)
# Check for missing packages and install only those
missing_packages <- packages[!(packages %in% installed.packages()[, "Package"])]
if (length(missing_packages)) {
install.packages(missing_packages, repos = "https://cran.rstudio.com/")
}
# Load all packages quietly
invisible(lapply(packages, library, character.only = TRUE))
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## Loading required package: betareg
# Optional: setwd("path/to/your/data")
##Loading the data
attitude_labeled_p2 <- read_dta("attitude_labeled-p2.dta")
endperiod_outcomes_labeled_p2 <- read_dta("endperiod_outcomes_labeled-p2.dta")
performance_labeled_p2 <- read_dta("performance_labeled-p2.dta")
summary_volunteer_labeled_p2 <- read_dta("summary_volunteer_labeled-p2.dta")
wage_new_labeled_p2 <- read_dta("wage_new_labeled-p2.dta")
The attitude_labeled_p2 dataset contains weekly
self-reported attitude measures such as exhaustion, positive mood, and
negative mood.
The following cleaning steps were performed:
The following cleaning steps were performed:
View(),
skim(), and head() to understand its structure
and variable types.year_week variable into separate year
and week components to prepare for creating a proper date
variable.date) based on the year–week information.personid and
date appear first in the dataset.exhaustion, negative,
positive).attitude_clean for further analysis.Reviewed the dataset using View(), skim(),
and head() to gain an initial understanding of its
structure, variable types, and overall data composition.
#Viewing the Data
# View(attitude_labeled_p2)
#skim the Data
# View(attitude_labeled_p2)
#head the Data
head(attitude_labeled_p2)
## # A tibble: 6 × 5
## personid year_week exhaustion negative positive
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 4122 202249 9 20 20
## 2 4122 202250 8 21 25
## 3 4122 202251 8 20 24
## 4 4122 202252 6 17 22
## 5 4122 202253 12 19 19
## 6 4122 202302 12 18 19
Checked for missing values, identified potential duplicate entries, and reviewed the distinct values of key variables to ensure data consistency
#Check for missing values
colSums(is.na(attitude_labeled_p2))
## personid year_week exhaustion negative positive
## 0 0 0 0 0
#Check for duplicates
attitude_labeled_p2[duplicated(attitude_labeled_p2), ] #give me all rows with duplicates, keep all columns
## # A tibble: 0 × 5
## # ℹ 5 variables: personid <dbl>, year_week <chr>, exhaustion <dbl>,
## # negative <dbl>, positive <dbl>
#No duplicates found
Check of Unique Values Across Variables
# See all distinct values in personid
unique(attitude_labeled_p2$personid)
## [1] 4122 8834 10356 12974 14522 16424 16514 16596 21654 23228 23772 24608
## [13] 25638 25864 25962 26328 27704 28190 28224 29172 30014 31136 31292 31888
## [25] 31936 33128 33278 33350 33354 36032 36288 36314 37292 37798 38566 38712
## [37] 38878 38898 39144 39164 39458 39466 39990 40316 40328 40456 40490 42108
## [49] 42152 42624 43258 43570 43926 44266 44282 44408 44782 44784 44800 45254
## [61] 45442
# See all disctinct values in year_week
unique(attitude_labeled_p2$year_week)
## [1] "202249" "202250" "202251" "202252" "202253" "202302" "202303" "202304"
## [9] "202305" "202306" "202307" "202308" "202309" "202310" "202311" "202312"
## [17] "202313" "202314" "202315" "202316" "202317" "202318" "202319" "202320"
## [25] "202321" "202322" "202323" "202324" "202325" "202326" "202327" "202328"
## [33] "202329" "202330" "202331" "202332" "202333" "202334" "202335"
# See all distinct values in exhaustion
unique(attitude_labeled_p2$exhaustion)
## [1] 9.00 8.00 6.00 12.00 10.00 11.00 14.00 13.00 18.00 15.00 16.00 19.00
## [13] 20.00 23.00 24.00 29.00 2.00 3.00 4.00 8.58 0.00 1.00 7.00 11.64
## [25] 6.32 5.00 6.40 17.00 25.00 27.00 7.55 22.00 9.55 21.00 31.00 9.56
## [37] 36.00 26.00 32.00 28.00 12.20 33.00 30.00 12.47 9.78 6.27 6.21 34.00
## [49] 35.00 7.04 6.86 8.55 5.99
# See all distinct values in negative
unique(attitude_labeled_p2$negative)
## [1] 20.00000 21.00000 17.00000 19.00000 18.00000 16.00000 24.00000 28.00000
## [9] 22.00000 26.00000 25.00000 27.00000 10.00000 15.00000 14.00000 13.00000
## [17] 17.55000 11.00000 12.00000 18.53000 9.00000 17.87716 8.00000 14.85000
## [25] 15.47000 23.00000 37.00000 16.22000 30.00000 17.98000 18.19000 32.00000
## [33] 29.00000 40.00000 18.77000 31.00000 19.75000 17.57000 14.89000 15.04000
## [41] 34.00000 39.00000 36.00000 35.00000 15.55000 15.94000 16.75610 15.10000
# See all distinct values in positive
unique(attitude_labeled_p2$positive)
## [1] 20.00000 25.00000 24.00000 22.00000 19.00000 23.00000 21.00000 18.00000
## [9] 17.00000 8.00000 16.00000 32.00000 34.00000 30.00000 29.00000 31.00000
## [17] 24.24000 28.00000 22.56000 27.00000 26.00000 28.13000 33.00000 35.00000
## [25] 9.00000 12.00000 14.00000 13.00000 15.00000 27.19000 11.00000 10.00000
## [33] 38.00000 26.28000 36.00000 23.79000 23.48000 37.00000 39.00000 40.00000
## [41] 21.91000 22.13000 23.46000 27.18000 26.88000 27.11000 26.04000 25.04065
## [49] 27.05000
Split the year_week variable into separate
year and week components.
# Transform into weekly data
attitude_labeled_p2 <- attitude_labeled_p2 %>% mutate(year=as.numeric(substr(year_week, 1, 4)))
attitude_labeled_p2 <- attitude_labeled_p2 %>%
mutate(week = as.numeric(substr(year_week, 5, 6)))
attitude_labeled_p2 <- attitude_labeled_p2 %>% dplyr::select(-year_week)
Created a proper ISO date variable (date) based on
year–week information.
# Add in a column with the date(with the day)
attitude_labeled_p2$date <- ISOweek::ISOweek2date(paste0(attitude_labeled_p2$year, "-W", sprintf("%02d", attitude_labeled_p2$week), "-1"))
Reordered columns to place personid and
date at the front.
# Re-positioning the columns
attitude_labeled_p2 <- attitude_labeled_p2 %>%
dplyr::select(personid, date, year, week, everything())
Verified whether attitude scores contained floats and counted non-integer values.
# Checking if values are integers or if they have any floats
all(attitude_labeled_p2$exhaustion == floor(attitude_labeled_p2$exhaustion))
## [1] FALSE
all(attitude_labeled_p2$positive == floor(attitude_labeled_p2$positive))
## [1] FALSE
all(attitude_labeled_p2$negative == floor(attitude_labeled_p2$negative))
## [1] FALSE
# Count non-integers in each column
sum(attitude_labeled_p2$exhaustion != floor(attitude_labeled_p2$exhaustion))
## [1] 21
sum(attitude_labeled_p2$positive != floor(attitude_labeled_p2$positive))
## [1] 21
sum(attitude_labeled_p2$negative != floor(attitude_labeled_p2$negative))
## [1] 22
Rounded decimal values in exhaustion,
negative, and positive (less than 1%
affected)
# Less than 1% of observations are in decimal floats. We will convert them
# to integers
attitude_labeled_p2$exhaustion <- as.integer(round(attitude_labeled_p2$exhaustion))
attitude_labeled_p2$negative <- as.integer(round(attitude_labeled_p2$negative))
attitude_labeled_p2$positive <- as.integer(round(attitude_labeled_p2$positive))
Assigned the cleaned output as attitude_clean for
further analysis.
#Assign a new variable attitude_clean
attitude_clean <- attitude_labeled_p2
rm(attitude_labeled_p2)
The endperiod_outcomes_labeled_p2 dataset contains
end-period outcome measures such as promotion status, job exit, and
commuting cost.
The following cleaning steps were performed:
View(),
skim(), and head() to understand structure and
data types.costofcommute → cost_of_commute_cny,
quitjob → quit_job).promote_switch and quit_job.cost_of_commute_cny) to numeric format and rounded
values.personid, cost_of_commute_cny,
promote_switch, quit_job.endperiod_clean for further analysis.Inspected the dataset using View(), skim(),
and head() to understand structure and data types
# Viewing and Analysing the Data
# View(endperiod_outcomes_labeled_p2)
skim(endperiod_outcomes_labeled_p2)
| Name | endperiod_outcomes_labele… |
| Number of rows | 135 |
| Number of columns | 4 |
| _______________________ | |
| Column type frequency: | |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| personid | 0 | 1 | 32716.79 | 10835.70 | 4122 | 25913.0 | 37292 | 40464 | 45442 | ▁▂▂▃▇ |
| promote_switch | 0 | 1 | 0.16 | 0.37 | 0 | 0.0 | 0 | 0 | 1 | ▇▁▁▁▂ |
| quitjob | 0 | 1 | 0.30 | 0.46 | 0 | 0.0 | 0 | 1 | 1 | ▇▁▁▁▃ |
| costofcommute | 0 | 1 | 7.40 | 7.22 | 0 | 2.5 | 6 | 10 | 55 | ▇▂▁▁▁ |
head(endperiod_outcomes_labeled_p2)
## # A tibble: 6 × 4
## personid promote_switch quitjob costofcommute
## <dbl> <dbl> <dbl> <dbl>
## 1 4122 0 0 18
## 2 6278 0 1 12
## 3 7720 0 0 9
## 4 8834 0 0 0
## 5 8854 0 0 4
## 6 10098 0 1 12
Checked for missing values, duplicate rows, and examined distinct
values of key variables (personid,
promote_switch, quitjob,
costofcommute)
# Check for missing values
colSums(is.na(endperiod_outcomes_labeled_p2))
## personid promote_switch quitjob costofcommute
## 0 0 0 0
# Check for duplicates
endperiod_outcomes_labeled_p2[duplicated(endperiod_outcomes_labeled_p2), ]
## # A tibble: 0 × 4
## # ℹ 4 variables: personid <dbl>, promote_switch <dbl>, quitjob <dbl>,
## # costofcommute <dbl>
# See all distinct values in personid
unique(endperiod_outcomes_labeled_p2$personid)
## [1] 4122 6278 7720 8834 8854 10098 10356 12426 12974 13980 14048 14220
## [13] 14522 14528 15444 16334 16422 16424 16514 16594 16596 17160 17906 19470
## [25] 21654 22284 23136 23228 23772 24324 24608 25520 25638 25864 25962 26328
## [37] 26634 26934 27704 28190 28224 28484 29172 29230 29808 30014 31136 31150
## [49] 31292 31888 31936 32320 32804 33128 33278 33350 33354 34890 35006 35344
## [61] 35822 36032 36288 36314 36494 36908 37276 37292 37294 37798 38038 38290
## [73] 38552 38566 38580 38712 38842 38862 38878 38898 39096 39144 39164 39458
## [85] 39466 39478 39634 39942 39990 40008 40034 40062 40162 40174 40192 40316
## [97] 40322 40328 40336 40346 40456 40472 40490 41286 41320 41332 42096 42104
## [109] 42108 42152 42308 42592 42618 42624 42628 42632 42634 42682 43258 43264
## [121] 43288 43524 43534 43570 43926 44256 44266 44282 44408 44782 44784 44794
## [133] 44800 45254 45442
# See all disctinct values in promote_switch
unique(endperiod_outcomes_labeled_p2$promote_switch)
## [1] 0 1
# See all distinct values in quitjob
unique(endperiod_outcomes_labeled_p2$quitjob)
## [1] 0 1
# See all distinct values in costofcommute
unique(endperiod_outcomes_labeled_p2$costofcommute)
## [1] 18.000000 12.000000 9.000000 0.000000 4.000000 6.000000 10.000000
## [8] 25.000000 11.818182 20.000000 5.000000 14.000000 2.000000 3.000000
## [15] 8.000000 17.727272 30.000000 55.000000 4.545455 16.000000 17.000000
Renamed variables to follow consistent naming conventions, converted
binary indicators (e.g., promote_switch,
quit_job) into factor labels, and cleaned
cost_of_commute_cny by converting it to numeric and
rounding values.
# Clean and format variables
endperiod_clean <- endperiod_outcomes_labeled_p2 %>%
rename(
cost_of_commute_cny = costofcommute,
quit_job = quitjob
) %>%
mutate(cost_of_commute_cny = round(as.numeric(cost_of_commute_cny)),
promote_switch = factor(promote_switch, levels = c(0, 1),
labels = c("Not Promoted", "Promoted")),
quit_job = factor(quit_job, levels = c(0, 1),
labels = c("Working", "Quit"))
) %>%
dplyr::select(personid, cost_of_commute_cny, promote_switch, quit_job, everything())
Reordered columns to position personid,
cost_of_commute_cny, promote_switch, and
quit_job at the front and saved the cleaned dataset as
endperiod_clean for further analysis.
# Assign a new variable endperiod_clean
endperiod_clean <- endperiod_outcomes_labeled_p2
rm(endperiod_outcomes_labeled_p2)
##The Performance Dataset
The attitude_labeled_p2 dataset contains weekly self-reported attitude measures such as exhaustion, positive mood, and negative mood. The following cleaning steps were performed:
View(),
skim(), and head() to understand its
structure and variable types.personid, year_week, perform1,
phonecall, phonecallraw, and
WFH_due_building_issues.WFH_due_building_issues into
interpretable factor labels to improve clarity.year_week and removed the original
variable.personid,
date, year, week, and key
performance measures at the front of the table.performance_clean for
subsequent analysis.We inspected the dataset using View(),
skim(), and head() to understand its structure
and variable types.
# Data Cleaning performance_labeled_p2
# Viewing and Analysing the Data
# View(performance_labeled_p2)
skim(performance_labeled_p2)
| Name | performance_labeled_p2 |
| Number of rows | 9870 |
| Number of columns | 12 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| numeric | 10 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| year_week | 0 | 1 | 6 | 6 | 0 | 86 | 0 |
| date | 0 | 1 | 10 | 10 | 0 | 85 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| personid | 0 | 1.00 | 32493.75 | 10623.78 | 4122.00 | 25864.00 | 36908.00 | 40328.00 | 45442.00 | ▁▂▂▃▇ |
| perform1 | 23 | 1.00 | -0.02 | 0.99 | -3.03 | -0.61 | 0.05 | 0.61 | 4.16 | ▁▆▇▁▁ |
| phonecall | 134 | 0.99 | -0.01 | 0.96 | -3.11 | -0.54 | 0.07 | 0.61 | 5.83 | ▁▇▃▁▁ |
| phonecallraw | 281 | 0.97 | 440.21 | 142.53 | 1.00 | 357.00 | 445.00 | 527.00 | 1264.00 | ▁▇▃▁▁ |
| WFH_due_building_issues | 0 | 1.00 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| logphonecall | 281 | 0.97 | 6.01 | 0.48 | 0.00 | 5.88 | 6.10 | 6.27 | 7.14 | ▁▁▁▁▇ |
| logcallpersec | 279 | 0.97 | -5.17 | 0.16 | -5.95 | -5.26 | -5.17 | -5.08 | -1.10 | ▇▁▁▁▁ |
| logcalllength | 279 | 0.97 | 11.17 | 0.52 | 2.48 | 11.05 | 11.27 | 11.44 | 12.12 | ▁▁▁▁▇ |
| logcall_dayworked | 279 | 0.97 | 9.47 | 0.46 | 2.48 | 9.33 | 9.55 | 9.73 | 10.36 | ▁▁▁▁▇ |
| logdaysworked | 0 | 1.00 | 1.69 | 0.28 | 0.00 | 1.61 | 1.79 | 1.79 | 1.95 | ▁▁▁▁▇ |
head(performance_labeled_p2)
## # A tibble: 6 × 12
## personid year_week perform1 phonecall phonecallraw WFH_due_building_issues
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 4122 202201 -1.14 -1.13 223 0
## 2 4122 202202 0.415 0.312 499 0
## 3 4122 202203 1.01 1.10 649 0
## 4 4122 202204 1.48 1.41 709 0
## 5 4122 202205 -0.115 -0.190 403 0
## 6 4122 202206 1.51 1.68 760 0
## # ℹ 6 more variables: logphonecall <dbl>, logcallpersec <dbl>,
## # logcalllength <dbl>, logcall_dayworked <dbl>, logdaysworked <dbl>,
## # date <chr>
Checked for missing values, duplicate rows, and examined distinct
values of key variables (personid, year_week,
perform1, phonecall,
phonecallraw, WFH_due_building_issues).
# Check for missing values
colSums(is.na(performance_labeled_p2))
## personid year_week perform1
## 0 0 23
## phonecall phonecallraw WFH_due_building_issues
## 134 281 0
## logphonecall logcallpersec logcalllength
## 281 279 279
## logcall_dayworked logdaysworked date
## 279 0 0
# Check for duplicates
performance_labeled_p2[duplicated(performance_labeled_p2), ]
## # A tibble: 0 × 12
## # ℹ 12 variables: personid <dbl>, year_week <chr>, perform1 <dbl>,
## # phonecall <dbl>, phonecallraw <dbl>, WFH_due_building_issues <dbl>,
## # logphonecall <dbl>, logcallpersec <dbl>, logcalllength <dbl>,
## # logcall_dayworked <dbl>, logdaysworked <dbl>, date <chr>
# See all distinct values in personid
unique(performance_labeled_p2$personid)
## [1] 4122 6278 7720 8834 8854 10098 10356 12426 12974 13980 14048 14220
## [13] 14522 14528 15444 16334 16422 16424 16514 16594 16596 17160 17906 19470
## [25] 21654 22284 23136 23228 23772 24324 24608 25520 25638 25864 25962 26328
## [37] 26634 26934 27704 28190 28224 28484 29172 29230 29808 30014 31136 31150
## [49] 31292 31888 31936 32320 32804 33128 33278 33350 33354 34890 35006 35344
## [61] 35822 36032 36288 36314 36494 36908 37276 37292 37294 37798 38038 38290
## [73] 38552 38566 38580 38712 38842 38862 38878 38898 39096 39144 39164 39458
## [85] 39466 39478 39634 39942 39990 40008 40034 40062 40162 40174 40192 40316
## [97] 40322 40328 40336 40346 40456 40472 40490 41286 41320 41332 42096 42104
## [109] 42108 42152 42308 42592 42618 42624 42628 42632 42634 42682 43258 43264
## [121] 43288 43524 43534 43570 43926 44256 44266 44282 44408 44782 44784 44794
## [133] 44800 45254 45442
# See all disctinct values in year_week
unique(performance_labeled_p2$year_week)
## [1] "202201" "202202" "202203" "202204" "202205" "202206" "202207" "202208"
## [9] "202209" "202210" "202211" "202212" "202213" "202214" "202215" "202216"
## [17] "202217" "202218" "202219" "202220" "202221" "202222" "202223" "202224"
## [25] "202225" "202226" "202227" "202228" "202229" "202230" "202231" "202232"
## [33] "202233" "202234" "202235" "202236" "202237" "202238" "202239" "202240"
## [41] "202241" "202242" "202243" "202244" "202245" "202246" "202247" "202248"
## [49] "202249" "202250" "202251" "202252" "202253" "202301" "202302" "202303"
## [57] "202304" "202305" "202306" "202307" "202308" "202309" "202310" "202311"
## [65] "202312" "202313" "202314" "202315" "202316" "202317" "202318" "202319"
## [73] "202320" "202321" "202322" "202323" "202324" "202325" "202326" "202327"
## [81] "202328" "202329" "202330" "202331" "202332" "202333"
# See all distinct values in perform1
unique(performance_labeled_p2$perform1)
## [1] -1.141609e+00 4.145924e-01 1.013977e+00 1.481231e+00 -1.150429e-01
## [6] 1.507072e+00 -1.030877e+00 -1.955392e+00 -7.384520e-01 1.173867e+00
## [11] 6.273752e-01 1.204250e+00 1.001966e+00 5.031173e-01 1.070303e+00
## [16] -1.024253e-01 2.910410e-01 9.243418e-01 3.310136e-01 1.222685e-01
## [21] 8.628694e-01 2.167489e-01 4.969600e-01 2.390567e-01 5.844755e-01
## [26] -8.526155e-01 -6.105604e-01 4.363570e-02 -2.616086e-01 -1.207018e+00
## [31] 2.235115e-01 1.781893e-01 3.657371e-01 -3.964069e-02 -1.086293e+00
## [36] -1.043393e+00 3.337389e-01 5.570192e-01 -3.136940e-01 6.697692e-01
## [41] -1.307252e+00 -1.792675e+00 -5.344517e-01 -3.660823e-01 -6.868713e-01
## [46] -2.076055e-01 -7.496964e-02 -9.480047e-01 -6.935334e-01 1.137831e+00
## [51] 6.866895e-02 -4.418268e-02 -1.662058e+00 -1.195511e+00 5.942659e-01
## [56] 1.450949e+00 -4.629852e-01 8.561057e-01 3.192649e-02 -4.246673e-02
## [61] 2.849833e-01 7.469885e-01 1.197690e+00 1.127738e+00 1.004186e+00
## [66] 1.944448e+00 8.153265e-01 9.191939e-01 6.017361e-01 1.721369e+00
## [71] 7.823189e-01 1.231101e+00 9.641125e-01 1.109164e+00 1.767903e+00
## [76] -1.090873e-01 9.384739e-01 1.039515e+00 5.171478e-01 6.690637e-01
## [81] 1.524333e+00 1.602966e+00 1.161553e+00 1.511311e+00 5.254246e-01
## [86] 9.832913e-01 -3.014971e+00 -5.778940e-01 6.117191e-01 1.332915e+00
## [91] 1.397783e+00 1.371181e+00 8.502410e-01 -1.046894e+00 1.120694e+00
## [96] 6.333149e-01 7.207848e-01 -2.993013e+00 -3.557002e-01 -3.030936e+00
## [101] -5.192714e-02 3.721918e-01 8.179675e-01 8.329278e-01 -2.017631e+00
## [106] 1.986185e+00 1.312585e+00 1.777946e+00 7.117965e-01 1.002217e+00
## [111] -2.057887e+00 1.356180e+00 -3.806635e-01 1.554316e-01 1.435184e-01
## [116] -1.190763e+00 6.012584e-02 -2.893288e-01 -9.207297e-01 -2.016746e+00
## [121] 3.420722e-01 -5.514197e-01 4.453201e-01 -2.496180e-01 -1.265147e-01
## [126] -1.603755e+00 -5.951015e-01 -6.387833e-01 -2.972709e-01 2.825060e-01
## [131] 2.507374e-01 1.117498e-01 -1.266213e+00 1.622431e-01 -1.569221e+00
## [136] -2.199287e+00 1.965479e-01 5.392096e-01 8.102859e-01 -3.011975e+00
## [141] 1.769641e+00 4.439972e-01 1.306738e-01 -1.071845e-01 7.214280e-01
## [146] -1.973695e-01 1.699122e+00 1.483564e+00 1.453965e+00 1.516200e+00
## [151] 1.469609e+00 9.136196e-02 -1.945142e+00 -2.999005e+00 4.211150e-01
## [156] 4.337223e-01 -7.189453e-02 8.665284e-01 -1.249544e+00 -1.941804e+00
## [161] -1.368623e+00 -1.177677e+00 4.490449e-01 -4.607845e-01 -4.597554e-02
## [166] -4.070449e-03 -1.933035e+00 -5.190176e-01 -4.391685e-01 -2.227921e+00
## [171] 7.038338e-01 4.084466e-01 1.166862e+00 -1.425208e+00 -2.398771e-01
## [176] -7.731633e-01 -6.334323e-01 -4.997342e-01 -1.051119e+00 -8.234745e-01
## [181] -1.491705e+00 -1.342020e+00 -3.904521e-04 -7.585653e-01 -6.101675e-01
## [186] -1.491099e-01 -9.622579e-02 2.221250e-01 -1.867324e-01 -2.903210e+00
## [191] -2.615312e-01 -2.734445e-01 -1.532275e+00 -1.087515e+00 -9.961801e-01
## [196] -8.015974e-01 -1.821097e-01 -4.600850e-01 -7.857132e-01 -2.183532e+00
## [201] -1.725260e+00 -8.510219e-01 -7.192738e-01 4.686704e-01 -1.111861e-01
## [206] -2.957422e+00 2.005292e-01 -6.650829e-01 -4.624942e-01 -3.384073e-01
## [211] -2.019940e-01 -2.306078e-01 6.389856e-01 4.286206e-02 4.766126e-01
## [216] 1.283209e-01 -9.423514e-02 7.823153e-01 4.442781e-01 8.827548e-01
## [221] -1.903435e+00 3.550518e-03 1.224995e+00 1.420284e+00 -7.860711e-01
## [226] -1.608076e+00 1.146315e+00 1.349824e+00 -1.502979e+00 3.837783e-01
## [231] 6.172434e-01 5.879799e-01 1.319272e+00 2.101417e+00 6.407382e-01
## [236] 1.630387e-01 1.943784e+00 -9.175046e-03 2.127277e-01 1.013614e+00
## [241] 1.401071e+00 1.169042e+00 6.148167e-01 -7.004597e-01 -2.738388e+00
## [246] 4.514410e-01 2.266164e-01 5.356417e-01 -1.522256e-01 8.454101e-01
## [251] -7.566691e-03 5.524273e-01 5.241294e-01 1.949514e-01 5.425987e-01
## [256] -2.957214e-01 -2.338022e-01 6.536863e-01 5.962580e-02 2.095425e+00
## [261] 2.678875e-01 1.330537e+00 9.521651e-01 7.266224e-01 5.992194e-01
## [266] -6.777723e-02 -2.766216e+00 2.925956e-01 2.640500e-01 2.431794e-01
## [271] 1.056073e+00 7.160758e-01 2.249328e-01 5.510164e-01 1.178426e+00
## [276] 8.473163e-01 -5.935936e-02 -2.752302e+00 -1.381593e+00 6.740867e-01
## [281] 3.549850e-01 9.917526e-01 1.080286e+00 1.229030e+00 9.461738e-01
## [286] 1.387107e+00 1.303402e+00 1.377056e+00 -2.731431e+00 2.020360e+00
## [291] 9.636522e-01 1.479008e+00 1.767163e+00 2.046206e+00 9.200047e-01
## [296] 3.292862e-01 -3.077039e-01 1.335275e-01 9.934111e-01 1.521963e+00
## [301] 2.553169e+00 2.228572e+00 1.990576e+00 1.953390e+00 1.767410e+00
## [306] 1.501835e+00 -2.333010e+00 -1.623399e+00 2.645581e+00 2.091417e+00
## [311] 1.800102e+00 3.321075e+00 2.359617e+00 7.460817e-01 2.258978e+00
## [316] 1.988559e+00 3.528104e+00 1.144897e+00 1.689471e+00 1.279451e+00
## [321] 2.166920e+00 1.330729e+00 1.347081e+00 8.767988e-01 1.319121e+00
## [326] 5.103847e-01 1.475074e+00 1.551486e+00 8.340002e-01 -3.575025e-01
## [331] -2.285366e+00 9.165697e-01 5.168449e-01 6.259617e-01 3.575610e-01
## [336] 3.404010e-01 -2.309188e+00 -4.468346e-01 2.023887e+00 5.253240e-01
## [341] 6.249517e-01 9.597723e-01 6.192997e-01 5.482372e-01 3.008319e-01
## [346] 1.638559e-01 2.001943e-01 -4.488919e-02 4.447735e-01 7.774730e-01
## [351] 2.785241e-01 4.090404e-01 8.710456e-01 2.036251e-01 1.324571e+00
## [356] -5.296523e-02 9.882364e-01 -2.327055e+00 8.231988e-01 -1.093763e+00
## [361] 8.847722e-01 1.601350e+00 1.455390e+00 1.349302e+00 2.269072e+00
## [366] 1.384530e+00 2.708971e+00 1.808784e+00 2.255041e+00 1.066567e+00
## [371] 1.577225e+00 1.050921e+00 1.260272e+00 1.143383e+00 7.939270e-01
## [376] -2.297277e+00 1.092610e+00 2.060491e-01 1.913964e+00 3.037432e+00
## [381] 1.337088e+00 -7.025172e-01 1.572785e+00 1.782841e+00 2.460254e+00
## [386] -2.513637e+00 6.522362e-01 -2.103188e-01 9.320402e-01 8.937542e-01
## [391] -7.079731e-01 4.091305e-01 -2.400529e+00 6.585907e-01 -2.279800e+00
## [396] 2.744065e-01 3.453723e-02 7.444525e-01 1.070464e+00 8.211296e-02
## [401] 3.990960e-01 1.226100e+00 -9.298251e-01 -8.289386e-02 9.194126e-01
## [406] 2.378098e-01 1.412402e+00 -3.334205e-01 7.663699e-01 -1.563551e+00
## [411] 1.355858e+00 8.831781e-01 7.547883e-01 8.348981e-01 -3.144383e-01
## [416] -1.376606e+00 7.041554e-01 9.410287e-01 6.539255e-01 -1.870983e+00
## [421] -3.473753e-01 9.599702e-01 1.145266e+00 -2.902460e-02 6.692684e-01
## [426] -1.394884e+00 1.200164e-01 5.890773e-01 -3.596205e-01 -7.808449e-01
## [431] -7.052782e-01 5.471519e-01 9.942343e-01 -3.071190e-01 -4.059098e-01
## [436] -1.729885e+00 -1.026234e+00 5.597446e-01 1.833110e+00 1.104521e+00
## [441] 1.065457e+00 1.437927e+00 6.578587e-01 8.661997e-01 6.416075e-01
## [446] -3.462976e-01 -1.783591e+00 1.497281e+00 1.729646e+00 1.263199e+00
## [451] 5.514673e-01 7.146884e-01 8.658973e-01 -3.411502e-01 4.276135e-01
## [456] -1.580438e-01 5.351145e-01 5.893204e-01 4.125730e-01 -1.839108e+00
## [461] -1.301498e+00 2.168495e-01 9.095040e-01 6.045626e-01 3.643236e-01
## [466] 1.135510e+00 2.263381e-01 -9.933269e-01 -7.381491e-01 -2.121480e-01
## [471] 1.876779e-01 2.581345e-01 2.456176e-01 1.989830e-01 1.759303e-02
## [476] 1.111789e+00 -4.847883e-01 4.352844e-01 8.111871e-01 8.401186e-02
## [481] -3.651123e-02 -1.580297e+00 -4.964975e-01 -2.874495e-01 -2.570671e-01
## [486] -2.954239e-01 -1.198883e-01 -4.612692e-01 -9.142313e-02 1.611305e-01
## [491] -3.014806e-01 -3.509821e-02 8.714083e-02 -1.867876e+00 -1.523467e+00
## [496] 2.573268e-01 1.623417e-01 1.767763e-01 1.385819e-02 3.162356e-02
## [501] 5.737756e-01 2.321924e-01 6.294947e-01 9.974226e-01 4.676867e-01
## [506] -3.540114e-02 -2.712382e-02 9.026397e-01 -6.729819e-02 5.325918e-01
## [511] 6.977904e-02 2.975012e-01 3.382809e-01 2.858931e-01 -6.710237e-01
## [516] 1.381703e+00 9.863198e-01 6.841032e-01 1.508343e-01 7.898892e-01
## [521] 1.261686e+00 2.854058e-01 8.369295e-01 7.338357e-01 8.815090e-01
## [526] 8.645378e-01 -1.390754e-01 2.787094e-01 1.100727e+00 3.315732e-01
## [531] -2.679690e+00 -2.879262e+00 -2.631792e+00 -2.855313e+00 -8.639916e-01
## [536] -2.535273e+00 2.462605e+00 3.343438e+00 1.057072e+00 -1.787133e+00
## [541] -1.573737e-01 6.625720e-01 -2.824045e+00 -2.895228e+00 -2.799433e+00
## [546] -3.006988e+00 -1.015635e+00 1.063539e+00 1.249573e+00 1.070807e+00
## [551] 9.468518e-01 1.573794e+00 6.207127e-01 1.934959e+00 1.028916e+00
## [556] 1.069495e+00 1.395129e+00 2.161975e+00 1.109265e+00 1.971399e+00
## [561] 1.564305e+00 1.239883e+00 7.914034e-01 1.670899e+00 3.001254e-01
## [566] 3.595793e-01 9.251499e-01 2.784240e-01 4.970606e-01 4.176207e-01
## [571] 4.908022e-01 2.959870e-01 -2.480200e-02 4.928211e-01 2.265399e-01
## [576] 1.302429e-01 8.562717e-02 -1.660948e+00 3.935964e-01 3.107246e-01
## [581] -2.597915e-01 -7.531890e-01 -1.226132e-01 -5.295047e-01 -2.291322e+00
## [586] -6.722354e-01 -1.839613e+00 -2.324329e+00 -2.321099e+00 -2.330285e+00
## [591] -2.303233e+00 -1.481576e+00 3.728027e-01 -6.415492e-01 -1.209037e+00
## [596] 4.330235e-04 -1.225390e+00 1.512378e-01 2.998225e-01 -1.087505e+00
## [601] -5.720427e-02 -3.701200e-01 -2.626191e-01 5.841725e-01 2.869020e-01
## [606] 9.648195e-01 7.971572e-01 1.082375e-01 1.093922e+00 2.534909e-01
## [611] 9.479624e-01 1.159735e+00 7.507243e-01 9.726927e-01 9.947988e-01
## [616] 7.596070e-01 6.821854e-01 6.243469e-01 1.068789e+00 1.096143e+00
## [621] 4.053056e-01 1.112698e+00 1.161112e-01 1.422787e+00 7.071181e-01
## [626] 1.496473e+00 8.237040e-01 1.590752e+00 -2.005412e+00 9.183462e-01
## [631] 1.986714e+00 2.687777e+00 1.901052e+00 6.978543e-01 1.198826e+00
## [636] 2.501624e+00 1.587497e+00 5.925102e-01 2.034224e+00 1.669048e+00
## [641] 1.899121e+00 2.605953e+00 1.169265e+00 1.819698e+00 2.679111e+00
## [646] 1.577916e+00 2.993048e-01 1.264978e+00 2.065939e+00 1.631862e+00
## [651] 7.309301e-01 -2.717517e+00 5.195096e-02 2.432090e-02 3.806588e-01
## [656] 1.132771e+00 7.529891e-01 -1.320442e+00 4.225976e-01 9.526353e-01
## [661] 3.072273e-01 1.030647e+00 7.604663e-01 2.321624e-01 4.546435e-02
## [666] 8.948514e-01 5.040018e-01 1.702224e+00 3.173538e-01 4.296800e-01
## [671] -1.947100e-01 1.057682e+00 -2.827481e-01 8.007718e-01 2.388717e-01
## [676] 2.458035e-01 3.988803e-01 2.398122e-01 3.722409e-01 1.361767e-01
## [681] -2.378873e-01 1.192835e+00 8.914341e-01 -7.081596e-01 4.792686e-01
## [686] -3.393331e-02 -2.164728e+00 2.016200e+00 9.262436e-01 1.004205e+00
## [691] 9.384488e-01 8.468210e-01 -2.052204e+00 1.167111e+00 1.322119e+00
## [696] 1.858866e+00 1.810376e-01 5.339581e-01 6.373710e-01 2.964438e-02
## [701] 8.235487e-01 4.893199e-01 1.477324e+00 1.186521e+00 2.462680e+00
## [706] 4.163310e+00 3.172136e+00 2.159200e+00 2.273903e+00 1.771248e+00
## [711] 2.638608e+00 -2.378086e+00 -2.027530e+00 -1.834609e+00 8.086515e-01
## [716] 7.775157e-01 9.457641e-01 7.121680e-01 1.585849e+00 9.879153e-01
## [721] 1.109713e-01 1.064667e+00 1.167380e+00 1.192131e+00 5.314597e-01
## [726] 8.360895e-01 1.249928e+00 1.068442e+00 1.142817e+00 1.150446e+00
## [731] 1.032987e+00 9.102312e-01 2.286628e-01 1.389262e+00 -1.175284e-01
## [736] 1.331387e+00 1.402608e+00 1.299241e+00 9.287976e-01 1.352517e+00
## [741] 1.699221e+00 -5.394295e-01 9.027581e-01 2.560232e-01 6.186830e-01
## [746] 8.817372e-01 2.805405e-01 3.038923e-01 1.513028e+00 -5.268918e-01
## [751] 8.553551e-01 6.377156e-01 9.851134e-02 1.627713e-01 7.452463e-01
## [756] -5.637914e-01 3.758585e-01 5.357013e-01 4.570849e-01 2.454739e-01
## [761] -1.415363e+00 6.466190e-01 6.933686e-01 7.194858e-01 3.853978e-01
## [766] -5.494607e-02 9.261100e-01 -1.706912e+00 -6.965929e-02 -2.244835e-01
## [771] -2.610264e-01 -6.379331e-01 8.011007e-01 6.196153e-01 3.034262e-01
## [776] 1.077625e+00 3.178286e-01 9.912245e-01 2.675049e-01 7.161765e-01
## [781] 1.631225e-04 7.352786e-02 1.090583e+00 6.235697e-02 -2.626486e-02
## [786] -3.595759e-01 6.993973e-01 1.052852e-01 1.240847e-01 6.192269e-01
## [791] 8.762525e-01 1.413701e+00 9.035351e-01 -7.989779e-02 1.547166e+00
## [796] 9.490318e-01 1.975669e+00 1.611734e+00 9.224290e-01 -3.596136e-02
## [801] 1.124093e+00 6.409965e-01 1.705538e+00 -1.118137e+00 2.360155e+00
## [806] 1.898153e+00 1.391449e+00 1.177448e-01 8.429013e-01 1.664215e+00
## [811] -2.055233e+00 -1.162033e+00 1.593716e+00 3.522651e-01 5.558796e-01
## [816] 2.161937e-01 1.466312e+00 2.005912e+00 2.640924e+00 1.101089e+00
## [821] 2.444368e+00 8.369498e-01 1.340214e+00 3.928024e-01 1.518210e+00
## [826] 7.205038e-01 1.627317e+00 2.102350e+00 1.098093e+00 4.667000e-01
## [831] 1.172915e+00 2.053084e+00 1.110379e+00 -2.571527e+00 9.949588e-01
## [836] -1.257828e+00 1.527822e+00 1.543144e+00 8.426407e-01 -1.644121e-01
## [841] -1.704609e+00 1.152948e+00 -6.626454e-02 9.637315e-01 1.380149e+00
## [846] 9.493940e-01 6.652058e-01 7.903592e-01 2.194566e+00 -3.093392e-02
## [851] -9.437798e-01 -7.306890e-03 1.195536e+00 6.499239e-01 2.677507e-01
## [856] 4.630200e-01 1.438019e+00 5.571457e-01 -2.640099e+00 -1.341254e+00
## [861] -7.008263e-01 -1.155604e+00 -8.143539e-01 -9.856784e-01 -9.740413e-01
## [866] -1.677951e+00 -1.220719e+00 -1.296616e+00 -1.061498e+00 -1.061265e+00
## [871] -1.186274e+00 -9.187768e-01 -1.057955e+00 -1.037835e+00 -1.321444e+00
## [876] -6.005674e-01 -1.053947e+00 -2.549200e-01 -9.573080e-01 -4.312631e-01
## [881] -6.601512e-01 -4.120751e-01 -5.316773e-01 -5.321435e-01 -6.277724e-01
## [886] -7.104750e-01 -1.075310e+00 -7.908466e-01 -9.069065e-01 -7.715810e-01
## [891] -2.681110e-01 -1.258862e+00 -6.698459e-01 -1.189972e+00 -8.903286e-01
## [896] -9.056634e-01 -8.869416e-01 -7.137066e-01 -1.483586e+00 -1.409444e+00
## [901] -8.050160e-01 -1.335691e+00 -9.754858e-01 -1.316892e+00 -1.417617e+00
## [906] -1.746453e+00 -6.350901e-01 -7.616216e-01 -4.263222e-01 -1.356286e-01
## [911] -6.786399e-01 -8.981903e-01 -5.498552e-01 -4.541487e-01 -4.852068e-01
## [916] -4.759466e-01 -6.938969e-01 -1.107502e+00 -5.915402e-01 -7.170935e-01
## [921] -4.764905e-01 -3.364572e-01 -6.505026e-01 -5.700532e-01 -5.228056e-01
## [926] -1.162145e+00 -1.618601e+00 -5.460021e-01 -1.361964e+00 -1.583301e+00
## [931] -5.114794e-01 -4.260891e-01 -3.239654e-01 -9.805821e-01 1.309123e+00
## [936] 1.299630e+00 1.560742e+00 1.183803e+00 3.628229e-01 -2.755842e-01
## [941] -7.254990e-01 1.695057e+00 -2.972477e+00 -2.918067e+00 -2.840150e+00
## [946] -2.964693e+00 8.162259e-02 9.169274e-01 1.184767e+00 1.334774e+00
## [951] -1.267857e+00 1.617606e+00 1.422386e+00 -2.662829e+00 3.159956e-01
## [956] 1.615462e+00 2.660467e+00 -6.849382e-02 9.570903e-01 -4.216921e-01
## [961] 1.583161e+00 2.823735e-01 9.994746e-01 3.757808e-01 1.200761e-01
## [966] 9.250998e-01 1.323836e+00 3.325418e-01 2.385684e+00 6.357272e-01
## [971] 1.076926e+00 3.488089e-01 -5.914626e-01 4.999354e-01 1.119543e+00
## [976] -1.687724e+00 7.268813e-01 -1.413608e+00 1.270048e+00 1.174652e+00
## [981] 1.487455e+00 -6.523356e-01 1.650762e+00 1.915105e+00 -2.605343e+00
## [986] -1.044609e+00 2.738042e+00 1.661000e+00 6.408234e-01 7.056272e-01
## [991] 2.031398e+00 6.212470e-01 5.441763e-02 4.610157e-01 6.835185e-01
## [996] 5.223867e-01 1.280784e+00 1.058204e+00 1.850302e+00 1.008890e+00
## [1001] 1.322081e+00 1.763357e+00 1.372949e+00 1.438218e+00 9.417125e-01
## [1006] 8.881242e-01 6.835651e-01 5.707789e-01 7.021852e-01 -7.328663e-01
## [1011] 4.643472e-01 6.366529e-01 6.815542e-01 -8.611636e-01 -2.932999e-01
## [1016] -1.361519e+00 2.597272e-01 8.205811e-01 8.053195e-01 1.395068e+00
## [1021] 4.134130e-01 1.527781e+00 1.280312e+00 -1.940818e+00 1.167546e+00
## [1026] 1.181521e+00 2.100640e+00 7.693253e-01 9.576648e-02 -8.752922e-01
## [1031] 9.220871e-01 9.878798e-01 -4.445174e-01 -9.581375e-01 5.847134e-01
## [1036] -2.638665e-01 -2.243143e-01 1.312968e-01 5.777360e-01 6.276240e-01
## [1041] 3.355748e-01 1.735234e-01 -2.967074e+00 -2.655023e-01 -1.115312e+00
## [1046] -8.691058e-01 -8.283287e-02 7.471218e-01 -9.077501e-02 4.413490e-01
## [1051] -1.544188e+00 -2.179560e+00 -1.116810e+00 6.998119e-01 1.081704e+00
## [1056] -1.259196e+00 -1.047618e+00 -2.255489e+00 9.902327e-01 -3.915927e-01
## [1061] 1.984573e-01 -3.413831e-01 4.382859e-01 5.677622e-02 1.018484e+00
## [1066] 4.915932e-01 1.255638e+00 6.236630e-01 1.170200e+00 -2.735997e-01
## [1071] 1.364101e+00 1.536427e+00 1.435727e+00 -1.992355e+00 2.040558e+00
## [1076] 1.160006e+00 7.707337e-01 9.038293e-01 1.438101e+00 7.212080e-01
## [1081] 1.744145e+00 1.407093e+00 1.497258e+00 2.047133e+00 1.805012e+00
## [1086] -4.075788e-01 1.760662e-02 1.315622e+00 1.059164e+00 2.467778e-01
## [1091] 6.961929e-01 6.346827e-01 1.679558e+00 9.177231e-01 7.640780e-01
## [1096] 1.365551e+00 -1.624760e+00 5.398934e-01 1.575760e+00 1.789649e+00
## [1101] 1.859223e+00 4.414243e-01 2.163860e+00 1.348880e+00 1.049129e+00
## [1106] 1.380450e+00 1.451331e+00 1.142652e+00 2.090023e+00 5.165474e-01
## [1111] 1.785065e+00 -6.020434e-03 1.799301e+00 1.096766e+00 2.244779e-01
## [1116] 4.124482e-01 9.237357e-01 -1.221231e+00 -8.679729e-01 1.151258e+00
## [1121] 1.881483e+00 -1.018151e-01 -1.402525e+00 -2.947106e+00 -2.663341e+00
## [1126] -2.302455e-01 -1.454502e-01 -7.851275e-01 -5.409554e-01 -4.281691e-01
## [1131] -1.949103e+00 -9.264869e-01 -1.695299e+00 -4.721055e-01 -3.240902e-01
## [1136] -1.969869e-01 -6.211262e-01 -2.524616e+00 6.675587e-01 -6.663693e-01
## [1141] 7.939778e-02 -9.039060e-01 -1.345680e+00 -2.663683e+00 -8.642928e-01
## [1146] -1.191690e-01 -5.356673e-01 1.393406e-01 -2.847987e-01 -4.444971e-01
## [1151] -1.579195e+00 -4.641225e-01 2.457723e-01 -4.767300e-01 -2.566621e-02
## [1156] -2.165920e-01 -1.058577e+00 -1.747072e-01 -9.322964e-02 -2.003250e-01
## [1161] -1.010036e+00 -2.291080e+00 -3.008315e+00 -1.629726e+00 3.715283e-01
## [1166] 7.906604e-01 2.394788e-01 7.579909e-02 5.931195e-01 -6.653638e-01
## [1171] 2.175411e-01 1.103849e-01 4.893013e-01 -1.191486e-01 1.082710e+00
## [1176] 4.646688e-01 5.238870e-01 -8.556837e-02 8.844442e-01 -6.720196e-01
## [1181] 7.072126e-01 -4.711000e-01 3.395764e-01 1.066744e+00 5.867243e-01
## [1186] 3.582370e-01 4.690123e-01 5.817782e-01 3.971460e-01 1.323926e+00
## [1191] 5.743776e-01 1.559699e-01 -2.499682e+00 2.680520e-01 2.766578e-01
## [1196] 1.631518e+00 -9.421481e-02 1.073742e+00 -2.449452e-01 -1.935832e+00
## [1201] -7.701672e-01 4.622955e-01 -3.746417e-01 -7.668495e-01 7.750163e-01
## [1206] -7.953547e-02 -9.065602e-01 3.719142e-02 -7.046351e-01 -2.552908e+00
## [1211] -3.966168e-02 -1.194702e-01 -8.979138e-01 -1.211257e+00 8.837603e-01
## [1216] -7.472037e-01 -2.345891e-01 -1.793318e-01 -1.156684e+00 -1.162013e+00
## [1221] -1.056224e+00 -1.727369e-01 -7.126180e-01 -3.895817e-01 2.481049e-01
## [1226] 1.319806e-01 -6.647409e-01 5.581474e-03 2.480236e-01 -2.318709e+00
## [1231] -2.201620e+00 -3.859424e-01 -1.479439e+00 -4.408781e-01 -2.905523e+00
## [1236] -1.749530e+00 -1.741869e+00 4.975448e-01 -1.059261e+00 3.684948e-02
## [1241] -5.339779e-01 -2.612326e-01 -7.998068e-01 3.954160e-01 6.795433e-01
## [1246] 6.651856e-01 4.610091e-01 -1.935101e+00 1.633929e-01 9.931670e-01
## [1251] 4.619481e-01 3.874956e-01 -5.098477e-01 6.509842e-01 -2.442153e-01
## [1256] 1.970609e-01 -1.966570e-01 3.454220e-01 -2.827465e-01 -3.628852e-01
## [1261] 6.259007e-02 -1.218699e+00 4.978754e-02 -2.432829e-01 -1.199433e+00
## [1266] 3.683077e-01 -3.198792e-01 -6.179764e-02 -2.278704e-01 -7.387360e-01
## [1271] -7.205581e-01 1.703998e-01 -4.332514e-01 -3.106508e-01 1.063730e-01
## [1276] -8.546027e-02 7.508181e-02 4.249339e-03 -2.442424e+00 -6.611295e-01
## [1281] -2.476022e-01 -3.628074e-01 6.397356e-01 -7.734917e-01 -1.138638e+00
## [1286] 1.003095e+00 -3.459505e-01 2.211897e-01 3.741492e-01 3.218371e-01
## [1291] -4.142966e-01 -1.009853e+00 -1.129999e+00 2.708141e-01 7.839328e-01
## [1296] 5.728340e-01 3.142086e-01 -4.215367e-01 8.971496e-01 1.045246e+00
## [1301] 1.043258e+00 -9.129035e-01 -5.965387e-02 1.046411e+00 -9.181551e-01
## [1306] -2.114479e-01 5.299058e-01 -4.676965e-01 -3.403103e-01 5.389329e-01
## [1311] -2.312574e-01 5.380782e-01 -1.037602e+00 2.987960e-01 8.646154e-01
## [1316] -3.046998e-01 3.807994e-01 -6.037213e-01 -2.634255e-02 -9.363330e-01
## [1321] -7.079109e-01 1.710214e-01 4.931616e-01 -5.811464e-01 -4.102104e-01
## [1326] 5.010232e-01 6.398910e-01 1.021382e+00 6.023698e-01 1.052748e+00
## [1331] 1.620922e+00 1.282282e+00 1.410330e+00 1.343531e+00 8.375726e-01
## [1336] 2.324405e-01 -1.000706e+00 -1.991028e+00 -2.336666e+00 -2.308052e+00
## [1341] -1.651240e+00 9.496344e-01 1.874102e+00 1.710785e+00 9.227506e-01
## [1346] 1.611994e+00 -1.484057e-01 8.256288e-01 7.061257e-01 5.987088e-01
## [1351] 2.399304e+00 1.502847e+00 2.412335e+00 1.635581e+00 2.487499e+00
## [1356] 1.530093e+00 3.771380e-01 1.527802e+00 2.070316e+00 2.321828e+00
## [1361] 8.551668e-01 -1.182041e-01 1.338846e+00 1.469816e-01 -9.148850e-01
## [1366] 8.398645e-01 -3.938071e-02 4.785829e-01 8.261907e-01 8.149105e-01
## [1371] 1.196803e+00 3.527660e-01 1.162136e+00 1.137584e+00 5.993521e-01
## [1376] -1.960017e-01 9.273547e-01 -1.337670e-01 -8.283800e-01 1.022185e+00
## [1381] 2.613962e-01 -8.137617e-01 -1.238341e-01 -1.405582e+00 -7.685185e-01
## [1386] -2.046640e+00 -1.645216e+00 1.596119e+00 1.330999e+00 1.174931e+00
## [1391] 1.115146e+00 9.626210e-01 2.004395e+00 1.335396e+00 9.184814e-01
## [1396] 1.773207e+00 1.864704e+00 2.028757e+00 1.402220e+00 2.046687e+00
## [1401] 1.856532e+00 1.796637e+00 1.601650e+00 1.633096e+00 2.044900e+00
## [1406] 1.763047e+00 1.517891e+00 5.157365e-01 1.793094e+00 1.568203e-01
## [1411] 1.784844e+00 1.529761e+00 2.215028e+00 1.483524e+00 1.938768e+00
## [1416] 1.255257e+00 2.040503e+00 1.796047e+00 1.565294e+00 -1.998885e-01
## [1421] 1.135655e+00 4.349762e-01 1.647530e+00 -1.032428e+00 5.193565e-01
## [1426] -2.665335e-02 1.242299e+00 1.157375e+00 -1.381608e-01 8.607622e-01
## [1431] 3.966781e-01 4.926954e-01 1.053807e+00 -1.206673e+00 3.212932e-01
## [1436] -2.697109e-01 3.774902e-01 4.687997e-01 1.540090e-01 1.039683e+00
## [1441] 1.607834e+00 5.332927e-01 8.049538e-01 9.050573e-01 8.649261e-01
## [1446] 1.155237e-01 3.021830e-01 1.108916e+00 3.817318e-01 1.333019e+00
## [1451] 3.337850e-01 -5.686546e-01 2.179029e+00 1.503068e+00 1.178272e+00
## [1456] 2.592548e-01 1.516058e+00 -1.802344e-01 2.490162e-01 3.337073e-01
## [1461] 1.541327e-01 5.562101e-01 -6.427964e-01 -7.784326e-01 4.424494e-01
## [1466] 7.068480e-02 1.274290e+00 1.476517e+00 2.193851e+00 1.392370e+00
## [1471] 1.765766e+00 8.845052e-01 1.665864e+00 1.667553e+00 1.708452e+00
## [1476] 8.332494e-01 -3.303838e-01 4.397146e-01 1.136640e+00 1.177540e+00
## [1481] 6.136895e-01 9.609756e-01 -3.041635e-01 4.915559e-02 8.152524e-01
## [1486] 1.307619e+00 1.033485e+00 8.408327e-02 1.137926e+00 1.195174e+00
## [1491] -2.983039e+00 -1.807080e+00 2.020791e+00 1.550382e+00 2.094326e+00
## [1496] 4.745813e-01 4.249979e-02 1.582415e-01 1.156225e+00 5.962210e-01
## [1501] 3.063325e-01 6.597582e-01 -8.055686e-01 6.359317e-01 -8.293950e-01
## [1506] 7.153533e-01 3.500143e-01 -9.871716e-02 -3.528660e-01 4.492912e-01
## [1511] 5.843077e-01 7.232953e-01 -1.131197e+00 -3.488949e-01 8.106591e-01
## [1516] 3.261878e-01 1.346754e+00 8.252259e-01 -6.936764e-01 2.683736e-01
## [1521] 1.176212e+00 5.002193e-01 2.450885e-01 3.478199e-01 1.228091e+00
## [1526] 5.696718e-01 9.305132e-02 -1.205164e-01 2.603704e-01 -1.435181e+00
## [1531] 5.979267e-02 4.925784e-01 1.116270e+00 -1.501154e-01 1.459937e+00
## [1536] 4.107182e-01 1.052769e+00 7.287459e-01 5.959527e-01 8.297819e-01
## [1541] 9.011264e-01 -1.048773e+00 -9.480478e-01 -1.563025e+00 -7.114533e-01
## [1546] -2.221397e+00 -2.949125e+00 -2.886854e+00 -1.770614e+00 1.584520e-01
## [1551] 8.434073e-01 -1.261161e+00 -1.863631e-02 1.347893e-01 2.672718e-01
## [1556] -5.295795e-01 -1.273186e+00 -2.697896e+00 -2.577827e+00 6.660860e-01
## [1561] -4.090449e-01 -7.777334e-01 1.374813e+00 1.609383e-01 2.917114e-01
## [1566] -1.778164e-02 -1.044298e+00 1.209952e+00 1.217814e+00 -2.158449e-01
## [1571] 1.655366e-01 1.835908e-01 -2.282581e+00 1.015975e+00 6.683391e-01
## [1576] 1.027379e+00 -2.188222e+00 1.309914e+00 -2.570744e-01 1.147256e+00
## [1581] 3.375455e-02 -1.325690e+00 -6.426005e-01 1.280923e+00 -1.268351e+00
## [1586] -2.530399e+00 9.120822e-01 1.917888e+00 5.339832e-01 1.338657e+00
## [1591] 4.919942e-01 4.367341e-01 1.442342e+00 -1.075874e-01 -1.080738e+00
## [1596] 6.876162e-02 -1.567350e+00 5.841424e-01 7.323912e-01 1.089149e+00
## [1601] 3.717706e-01 -1.729638e+00 1.352111e-01 3.118078e-01 -1.267906e+00
## [1606] 1.553132e+00 8.475138e-01 3.516537e-02 3.708050e-01 2.623665e-01
## [1611] -5.977647e-01 -6.705929e-02 -1.440554e-01 -6.920669e-01 -4.398460e-02
## [1616] -4.290906e-01 2.660064e-01 6.011757e-01 2.628618e-01 -1.934716e-01
## [1621] 3.324308e-01 7.292466e-01 3.251618e-02 2.769982e-01 2.470168e-01
## [1626] 1.368696e-01 3.941023e-01 -1.351364e+00 1.401096e+00 6.628222e-01
## [1631] -8.072540e-02 -2.286585e+00 -1.718941e+00 1.264558e+00 1.002077e+00
## [1636] 1.245098e+00 6.054583e-01 1.292360e+00 1.279882e+00 2.316420e-01
## [1641] 1.361459e+00 1.580812e+00 1.571429e+00 -6.145504e-01 7.978749e-01
## [1646] 6.870349e-01 7.595007e-01 8.185481e-01 -1.282735e+00 1.111011e+00
## [1651] -5.972944e-01 1.134258e+00 2.832372e-01 1.891199e+00 1.911154e+00
## [1656] 9.023036e-01 1.138615e+00 1.906376e+00 1.470665e+00 -1.750988e+00
## [1661] 1.575409e+00 2.718561e+00 2.283406e+00 2.200837e+00 1.769316e+00
## [1666] 1.984118e+00 1.862585e+00 2.736629e+00 2.715331e+00 3.509996e-01
## [1671] 8.100775e-01 1.992900e+00 1.320534e+00 1.074744e+00 2.199828e+00
## [1676] 2.040302e-01 1.025990e+00 5.899262e-01 1.105430e+00 5.569186e-01
## [1681] -2.193712e+00 8.242092e-01 4.647595e-01 2.895273e-01 8.405616e-01
## [1686] 5.070539e-01 -1.682388e-01 7.088340e-01 7.069157e-01 3.802724e-01
## [1691] 1.460841e+00 3.133488e-01 7.263976e-01 5.886133e-01 5.289582e-01
## [1696] 6.053703e-01 -6.539643e-01 7.438605e-01 -1.467381e-01 3.089070e-01
## [1701] 1.269963e+00 7.356843e-01 5.483970e-02 4.677873e-01 9.485673e-01
## [1706] 6.065805e-01 -2.503035e-01 7.064105e-01 3.010940e-02 3.912746e-01
## [1711] -1.600282e+00 -6.209577e-01 1.033560e+00 9.443282e-01 7.751521e-01
## [1716] 7.202403e-01 6.490772e-01 1.837350e+00 1.148228e+00 1.900538e+00
## [1721] 7.391162e-01 7.148907e-01 1.400277e+00 1.011151e+00 1.169426e+00
## [1726] 4.853519e-01 9.081922e-01 1.572950e-01 1.184366e+00 1.448426e+00
## [1731] 2.310055e+00 2.120993e+00 1.405930e+00 1.060915e+00 1.767095e+00
## [1736] 1.780621e+00 -4.377910e-02 8.257229e-01 1.102199e+00 1.174978e+00
## [1741] 1.431770e+00 2.761016e-01 5.372350e-01 1.308622e+00 6.735045e-01
## [1746] 1.056372e+00 1.180630e+00 1.867026e+00 1.133188e+00 1.365755e+00
## [1751] 8.898204e-01 1.523728e+00 1.999460e+00 1.202232e+00 8.417728e-01
## [1756] 9.886410e-01 1.114312e+00 9.660307e-01 -1.386631e-01 1.009637e+00
## [1761] 4.026814e-01 7.738398e-01 7.129729e-01 6.489766e-01 6.897563e-01
## [1766] -1.564549e+00 5.356202e-01 3.635164e-01 8.807359e-01 2.160424e-01
## [1771] 2.660082e-01 3.759322e-01 1.542667e-01 -3.835447e-01 9.837961e-01
## [1776] -2.175983e-01 5.993131e-01 1.134399e+00 2.955834e-01 2.782217e-01
## [1781] -5.791676e-01 -9.107575e-01 -3.403421e-01 1.055121e-01 -2.970387e-01
## [1786] 1.465952e-01 -2.288028e-01 1.369049e-01 2.035255e-01 6.950053e-01
## [1791] 5.557074e-01 5.790241e-01 3.361614e-01 5.243145e-01 3.646265e-01
## [1796] 7.244799e-01 6.620988e-01 6.435258e-01 4.552715e-01 4.598140e-01
## [1801] 1.076258e+00 4.817177e-01 9.015301e-01 1.229485e+00 6.372673e-01
## [1806] 6.701738e-01 1.711237e-01 -1.957410e+00 6.991442e-01 9.282788e-01
## [1811] -2.382913e-01 -1.925412e+00 8.021037e-01 3.287929e-01 1.514037e+00
## [1816] 1.433385e+00 1.096648e+00 1.131775e+00 5.858885e-01 1.675441e+00
## [1821] 1.505356e+00 -1.596144e+00 -2.994617e-01 4.564822e-01 6.544269e-01
## [1826] 1.057281e+00 5.267370e-01 1.042341e+00 1.414711e+00 4.428552e-01
## [1831] 8.463148e-01 8.667049e-01 1.939358e-01 1.232311e+00 1.115624e+00
## [1836] 2.771105e-01 -1.206959e-01 7.788866e-01 1.208187e+00 3.739128e-01
## [1841] -7.183648e-01 6.423135e-01 2.276500e-01 4.222219e-02 4.930229e-01
## [1846] 1.002221e-02 1.337754e-01 2.809465e-01 2.942706e-01 4.929217e-01
## [1851] 3.010337e-01 2.770099e-01 -3.899043e-01 -6.471011e-01 -1.086838e-01
## [1856] -4.051466e-01 -1.299822e-01 6.584242e-02 -3.529601e-01 1.005195e+00
## [1861] -1.305334e+00 3.323836e-02 7.250392e-02 2.990761e-02 3.085039e-01
## [1866] -1.344196e+00 -5.596862e-01 -7.549055e-01 -1.525688e+00 -1.254057e+00
## [1871] -4.690807e-02 -6.373095e-01 -1.868118e-01 -1.639993e-01 4.807654e-02
## [1876] 9.955656e-02 -1.137684e-02 -1.759466e+00 -6.124204e-02 4.039932e-01
## [1881] -1.245319e-01 -1.301297e+00 3.235439e-01 2.103893e-01 5.862920e-01
## [1886] 7.023737e-01 -4.037331e-01 1.580010e-01 7.211486e-01 -4.513037e-03
## [1891] -2.369795e-01 -7.944739e-01 -1.037438e+00 8.128647e-02 -2.245637e-01
## [1896] 7.008197e-02 1.501278e-01 3.845119e-01 -1.740893e+00 5.326925e-01
## [1901] 1.446770e-01 6.842044e-01 4.113618e-01 1.926239e-01 9.337385e-01
## [1906] 1.730357e+00 1.778381e+00 1.427560e+00 2.050353e+00 1.118260e-01
## [1911] -2.439115e+00 2.781002e+00 1.770908e+00 1.883348e+00 7.998115e-01
## [1916] 2.954868e-01 -1.705110e+00 1.100899e+00 2.072462e+00 -2.988045e+00
## [1921] -1.107424e+00 1.972669e+00 2.882970e+00 2.224132e+00 2.134889e+00
## [1926] 1.743004e+00 2.232693e+00 1.441450e+00 2.645677e+00 4.702218e-02
## [1931] -1.006397e-01 1.451766e+00 1.224743e+00 1.725448e+00 1.192675e+00
## [1936] -9.048865e-01 1.200490e+00 3.985111e-01 -2.855718e+00 -2.886932e+00
## [1941] 6.030692e-01 1.606001e+00 1.049565e+00 1.198580e+00 1.879495e+00
## [1946] 9.307400e-01 1.918958e+00 1.974456e+00 1.955657e+00 1.489475e+00
## [1951] 1.705871e+00 1.013831e+00 2.271147e+00 -2.769510e-01 2.294343e+00
## [1956] 2.250126e+00 1.589578e+00 -7.686285e-01 1.998580e-01 2.178128e+00
## [1961] 6.373588e-01 8.860565e-01 3.314857e-01 6.055238e-01 5.408753e-01
## [1966] 1.875952e+00 1.826328e+00 1.242253e+00 1.268681e+00 1.790562e+00
## [1971] 2.102075e+00 2.246661e+00 1.819166e+00 -1.902600e+00 -7.957864e-01
## [1976] -4.655088e-01 -3.405443e-01 -5.810857e-01 -8.514047e-01 -7.298722e-01
## [1981] -6.160117e-01 -8.747221e-01 -6.338777e-01 -4.130198e-01 2.886180e-01
## [1986] -2.781633e-01 -1.401772e-01 -7.535931e-01 1.801071e-01 3.371703e-01
## [1991] -2.599943e-01 -3.805169e-01 5.210840e-01 5.131095e-01 -1.501710e-01
## [1996] 5.271967e-02 1.737475e-01 -4.085790e-01 -2.569660e-01 -1.042586e+00
## [2001] 1.698666e-02 5.770053e-01 -7.720655e-01 1.009691e-01 2.990144e-01
## [2006] -8.614987e-01 -1.184307e+00 -4.236609e-02 4.464466e-02 -8.987459e-01
## [2011] -7.295126e-02 -6.056148e-01 -6.498269e-01 -5.622103e-01 -8.879449e-01
## [2016] -6.690053e-01 -1.206917e+00 -3.363048e-01 -1.103050e+00 -6.768786e-01
## [2021] -5.716984e-01 -3.969704e-01 6.281409e-02 -2.044766e-01 -4.630863e-01
## [2026] -1.184753e-01 -3.736531e-01 -3.564931e-01 -4.757043e-01 1.908063e-01
## [2031] -8.313175e-01 -1.193836e-01 -2.650410e-01 1.432640e-01 1.691044e-01
## [2036] 8.532374e-02 2.072598e-01 8.431429e-02 3.990038e-02 1.033256e+00
## [2041] 5.615612e-01 3.430247e-01 1.060163e-01 3.675537e-01 -2.262352e+00
## [2046] -1.089867e-01 -9.737864e-02 1.604235e-01 5.685859e-02 -9.358462e-03
## [2051] 3.192032e-01 5.239099e-01 9.420642e-02 7.013633e-01 9.228816e-02
## [2056] 2.306778e-01 -4.851924e-01 -2.254242e+00 -4.218475e-01 -3.946425e-01
## [2061] 2.712803e-01 -1.655212e-01 -1.857969e-01 -6.203769e-01 -2.415276e-01
## [2066] -6.136031e-01 -2.445261e-01 -4.940785e-01 2.474623e-01 -1.824099e-01
## [2071] -1.829538e-01 -8.335419e-01 -1.618175e-02 -3.564998e-01 -4.069902e-02
## [2076] 3.978895e-01 -4.734143e-01 8.185569e-02 -1.153973e+00 -2.258821e-01
## [2081] 4.399630e-01 -3.924210e-01 -1.559042e-01 -1.820991e-01 -6.663812e-01
## [2086] 8.420864e-01 -2.486900e-01 1.787480e-02 -1.557184e+00 5.237534e-01
## [2091] -1.639990e-01 -3.156694e-01 -9.002880e-01 7.929282e-01 7.185534e-01
## [2096] 1.027891e+00 1.468919e-02 9.338330e-02 1.052066e+00 -1.603789e-01
## [2101] -1.780587e+00 -1.923541e+00 -1.168841e+00 -5.242042e-01 1.764169e-02
## [2106] -1.441279e+00 1.684574e-01 -1.085704e+00 -1.220564e+00 2.471515e-01
## [2111] -7.813851e-01 -7.058449e-01 -1.597891e-01 -3.717109e-01 -9.039399e-01
## [2116] -5.027948e-01 -1.012883e+00 -2.083306e+00 -7.490062e-01 -1.121781e+00
## [2121] -1.272286e+00 -1.707533e+00 -2.373456e+00 -2.558639e+00 -2.752461e+00
## [2126] -2.926395e+00 -2.988201e+00 -1.535580e+00 -2.180489e+00 -8.550385e-01
## [2131] -1.336525e+00 -1.888307e-01 6.489760e-01 6.350461e-01 -2.309693e+00
## [2136] -4.185712e-01 4.682925e-01 9.617912e-01 1.540649e-01 1.277193e-01
## [2141] -2.722447e-02 8.764606e-02 3.007313e-01 1.061179e-01 -7.678623e-02
## [2146] -1.774607e+00 -1.568385e+00 -1.721210e+00 -2.708954e-01 -3.055178e-01
## [2151] -1.147399e-01 -7.466671e-02 -1.042889e+00 -2.804846e-01 2.657685e-02
## [2156] -3.167223e-01 -2.450545e-01 -1.010487e+00 -5.197136e-01 -8.485782e-01
## [2161] -2.040182e+00 -9.598145e-01 -5.886561e-01 -1.037236e+00 -3.070325e-01
## [2166] -7.174565e-01 -7.875090e-01 -7.304776e-01 -6.427603e-01 -1.034713e+00
## [2171] -6.202507e-01 -1.459066e+00 -5.766439e-01 -4.305829e-01 -3.325700e-01
## [2176] -5.087110e-01 -7.250268e-01 -3.486193e-01 -5.103263e-01 -1.029363e+00
## [2181] -8.546349e-01 -3.606315e-01 -3.637605e-01 1.242874e-01 -2.765480e-01
## [2186] -2.757403e-01 -3.072338e-01 -5.839118e-01 -6.682983e-01 -1.150694e+00
## [2191] 1.225952e+00 1.870720e-01 1.321606e-01 -2.561578e-01 -2.476787e-01
## [2196] -5.242562e-01 -3.481146e-01 -7.031226e-01 -7.213931e-01 -1.513812e-01
## [2201] 1.234403e-02 2.330001e-01 9.935477e-02 3.727613e-02 -1.902437e-01
## [2206] -8.581674e-01 -2.372819e-01 -1.034006e+00 -5.027560e-01 -2.012464e-01
## [2211] -3.086473e-01 -3.265138e-01 -2.729143e-01 -6.164152e-01 9.416346e-03
## [2216] -8.849595e-02 2.460212e-01 -6.687024e-01 -1.767238e+00 -1.124953e+00
## [2221] -4.695465e-01 -5.839123e-01 -1.029363e+00 4.045991e-01 -6.962000e-02
## [2226] -1.054396e+00 -4.139281e-01 -1.196016e+00 6.675072e-02 -7.123532e-02
## [2231] -1.682145e+00 -1.343793e+00 -7.230084e-01 -1.119907e+00 -1.222563e+00
## [2236] -9.074267e-01 -4.971034e-01 -6.033936e-01 -8.990487e-01 -1.064389e+00
## [2241] -9.016730e-01 -1.859902e+00 -1.302104e+00 -1.018966e+00 -8.494869e-01
## [2246] -1.023609e+00 -4.407790e-01 -1.199448e+00 -6.376130e-01 -1.205202e+00
## [2251] -8.466604e-01 -8.448434e-01 -1.224582e+00 -1.012001e+00 -1.250119e+00
## [2256] -1.607953e+00 -1.113648e+00 -7.629805e-01 -8.087068e-01 -5.091150e-01
## [2261] -1.094066e+00 -2.869448e-01 -1.962356e+00 -7.011042e-01 -1.094268e+00
## [2266] -8.109275e-01 -1.134341e+00 -3.856648e-01 -5.300099e-01 -4.375483e-01
## [2271] -7.048390e-01 -5.239532e-01 -1.175828e+00 -5.804805e-01 -5.982902e-02
## [2276] 1.042579e-02 -6.925244e-01 -6.869725e-01 -1.955896e+00 -6.238844e-01
## [2281] -1.010992e+00 -5.994570e-01 -3.536665e-01 -9.125746e-01 -4.377501e-01
## [2286] 4.471959e-01 -3.960621e-01 -5.720013e-01 -3.111709e-01 -6.357959e-01
## [2291] -5.457569e-01 -9.579980e-01 -2.520185e+00 4.641696e-01 5.503922e-02
## [2296] -9.055655e-02 9.599333e-01 4.590733e-01 -1.605343e-01 9.737920e-01
## [2301] 1.829375e-01 -1.856415e-01 -1.705398e-01 3.469442e-01 -1.119659e-01
## [2306] 3.010175e-01 -6.933531e-01 4.018980e-01 7.032963e-01 1.739421e-01
## [2311] -6.040320e-01 4.762729e-01 3.982003e-01 2.209566e-01 1.115929e-01
## [2316] 6.919701e-01 4.280929e-01 6.039698e-01 5.608085e-01 6.597006e-01
## [2321] -2.241174e-02 -1.666593e+00 6.366595e-01 -1.040833e+00 4.459917e-01
## [2326] 3.091900e-01 3.727824e-01 9.333736e-02 4.500002e-01 6.049798e-01
## [2331] -1.807783e-01 -7.903028e-01 -4.181497e-01 2.556347e-01 -1.219490e+00
## [2336] -5.727867e-01 2.842382e-01 -2.764848e-01 7.368325e-02 -1.229947e+00
## [2341] -1.122915e+00 6.174716e-01 -3.350586e-01 -7.147625e-01 -4.698402e-01
## [2346] -1.456340e-01 3.948134e-01 -1.241469e-01 -8.193408e-01 -8.302326e-01
## [2351] -2.041301e-01 -2.793596e-01 -5.724524e-02 -2.814257e-01 -1.423931e-02
## [2356] 2.685150e-01 -7.588927e-02 4.905199e-01 5.686701e-01 -1.850976e-01
## [2361] 7.793805e-01 5.811536e-02 8.233188e-01 -7.566029e-01 9.149390e-01
## [2366] 6.973771e-01 -1.058913e-01 -8.229608e-01 -2.734863e-01 -4.607436e-02
## [2371] 1.313247e-01 -3.676706e-01 -5.999458e-01 -9.829590e-01 3.145193e-01
## [2376] -6.924985e-01 -2.795069e+00 -7.347960e-01 2.395726e+00 2.276988e+00
## [2381] 9.736034e-01 -1.304398e+00 -5.180121e-01 -1.855319e+00 -4.907255e-01
## [2386] 1.028861e+00 -2.032913e+00 -2.791450e+00 -2.919176e+00 -1.318210e+00
## [2391] 1.139636e+00 1.605298e+00 -1.964747e+00 2.032474e+00 3.509109e+00
## [2396] 2.901322e+00 1.773643e+00 2.171883e+00 1.742737e+00 2.499845e+00
## [2401] 3.922570e+00 1.159241e+00 1.054820e+00 -9.588384e-02 1.197487e+00
## [2406] 8.814887e-01 1.616961e+00 8.861539e-01 -4.923944e-01 2.421127e-01
## [2411] 9.306725e-01 1.692487e+00 5.311860e-01 6.465656e-01 1.118321e+00
## [2416] 1.887394e+00 -4.535264e-02 1.761980e+00 1.126324e+00 9.133795e-01
## [2421] 5.601012e-01 1.174845e+00 8.262314e-01 9.423557e-01 4.629997e-01
## [2426] 1.100226e-01 9.153703e-01 9.636905e-02 1.506209e-01 1.316184e+00
## [2431] 1.041810e+00 7.447740e-01 1.523177e+00 -4.398490e-02 5.541495e-01
## [2436] 8.385578e-01 1.284293e+00 7.121586e-01 -1.087553e+00 9.423150e-01
## [2441] 1.405725e+00 9.569537e-01 1.145567e+00 1.307537e+00 1.283413e-01
## [2446] -6.609298e-01 -9.445146e-02 1.676913e-01 1.051080e-01 4.661725e-01
## [2451] -1.036371e-01 -7.651006e-01 -1.473905e+00 7.613862e-02 4.598134e-01
## [2456] 2.940289e-02 3.580657e-01 8.399557e-01 5.198732e-01 3.418140e-01
## [2461] 5.433499e-02 1.975695e-01 2.457182e-01 -1.381589e-01 -9.910057e-01
## [2466] 1.222675e-01 -2.583790e-01 -2.651847e-02 -4.803475e-01 1.631489e-01
## [2471] -5.258715e-01 -1.511218e-02 -2.439444e-01 -4.177641e-01 -1.433024e+00
## [2476] -3.746626e-01 -3.137951e-01 5.712516e-01 -5.173923e-01 -1.896442e+00
## [2481] -1.567314e-01 -1.087849e-01 -4.293722e-01 3.613959e-01 -1.772226e-01
## [2486] -4.198841e-01 -6.517916e-02 -5.883536e-01 -7.385531e-01 -8.306111e-01
## [2491] -1.078925e+00 -7.896290e-01 -9.137858e-01 -8.687660e-01 -8.206179e-01
## [2496] -7.779198e-01 -9.753598e-01 -1.118594e+00 -6.288304e-01 -6.874772e-01
## [2501] -3.851600e-01 -1.049955e+00 -2.769520e-01 -2.617098e-01 -6.341806e-01
## [2506] -3.871789e-01 -7.247244e-01 -3.234855e-01 -2.366765e-01 2.425893e-01
## [2511] -9.324022e-02 1.860197e-02 -1.420955e-01 -3.193471e-01 -3.338226e-02
## [2516] -8.126434e-01 5.100423e-02 -2.589844e-01 -2.242608e-01 -2.327399e-01
## [2521] 1.038973e-01 -2.028612e-01 2.869587e-02 4.642548e-01 3.330319e-01
## [2526] 3.614971e-01 4.535115e-02 -4.135665e-02 -4.866054e-01 -2.255589e+00
## [2531] -4.854948e-01 -6.749604e-01 -6.690048e-01 -5.253667e-01 -5.795717e-01
## [2536] -4.756027e-01 -8.348515e-03 1.636541e-01 -7.845818e-01 -9.939327e-01
## [2541] -3.471052e-01 -3.744603e-01 -7.402691e-01 2.685308e-01 -1.185417e+00
## [2546] -1.265362e+00 -1.212166e+00 -1.191978e+00 -9.656694e-01 -2.188968e+00
## [2551] -1.583527e+00 -1.530634e+00 -9.234762e-01 -1.322191e+00 -7.794340e-01
## [2556] -1.139994e+00 -1.325623e+00 -1.614212e+00 -1.248000e+00 -1.549812e+00
## [2561] -6.548731e-01 -1.167449e+00 -1.429088e+00 -1.331579e+00 -1.764917e+00
## [2566] -1.467748e+00 -1.838906e+00 -1.812359e+00 -1.735038e+00 -1.729487e+00
## [2571] -2.207541e+00 -1.526899e+00 -1.735442e+00 -1.819727e+00 -1.575249e+00
## [2576] -1.629252e+00 -8.603881e-01 -8.217279e-01 -1.690322e+00 -1.452102e+00
## [2581] -7.884174e-01 -4.184706e-01 -4.198836e-01 -1.040769e+00 -7.674218e-01
## [2586] -4.596539e-01 -6.327671e-01 -1.396080e+00 -1.635006e+00 -1.234676e+00
## [2591] -1.078117e+00 -2.056332e+00 -1.320475e+00 -8.927904e-01 -1.098002e+00
## [2596] -1.142517e+00 -9.863620e-01 -7.195760e-01 -1.210551e+00 -9.650636e-01
## [2601] -9.294317e-01 -8.832006e-01 -1.191170e+00 -5.530242e-01 -5.230449e-01
## [2606] -9.794981e-01 -7.501612e-01 -8.348506e-01 -2.918909e-01 -1.127275e+00
## [2611] 8.495571e-01 2.637288e-01 1.524183e+00 9.014561e-01 1.019872e+00
## [2616] -5.116373e-01 5.940437e-01 6.116988e-01 3.545367e-01 4.283530e-01
## [2621] 1.761699e+00 -3.114625e-01 -1.600005e+00 -9.700435e-03 6.782161e-01
## [2626] -1.190004e+00 1.431343e+00 1.731054e+00 1.333196e+00 1.387810e+00
## [2631] 4.496881e-01 1.819207e+00 1.553761e+00 1.868192e+00 6.060079e-01
## [2636] 5.295170e-01 -4.381426e-01 1.591384e+00 4.384721e-02 5.183010e-02
## [2641] 4.645874e-01 -7.748526e-01 6.748781e-01 -6.745231e-01 -6.228991e-01
## [2646] -3.330106e-01 -4.918536e-01 -1.503411e-01 -2.456469e-01 2.308821e-01
## [2651] -2.575602e-01 -1.413143e+00 -7.499735e-01 -6.983495e-01 -4.243453e-01
## [2656] 2.110267e-01 3.857540e-01 2.983903e-01 -1.349606e+00 3.409035e-01
## [2661] -4.408984e-01 1.331205e+00 1.417026e+00 1.169235e+00 3.055932e-01
## [2666] -4.278881e-01 1.605941e+00 -9.202340e-01 -1.025600e-01 2.347527e-01
## [2671] 3.219416e-01 -4.500647e-01 1.375345e+00 2.118369e+00 7.646542e-01
## [2676] 2.089903e+00 -4.205901e-01 -8.091103e-01 -1.269539e-01 -7.075639e-01
## [2681] 2.012583e+00 2.862806e+00 1.372922e+00 1.337391e+00 5.331977e-01
## [2686] 2.109890e+00 -3.783969e-01 1.242608e+00 4.757284e-02 2.533134e+00
## [2691] -5.082057e-01 5.879078e-01 6.093069e-01 2.395615e-01 6.423145e-01
## [2696] 8.230992e-01 1.809692e+00 5.467235e-01 7.088345e-01 9.417045e-01
## [2701] 6.602822e-01 -6.646641e-01 -1.285853e+00 3.270769e-01 2.500595e-01
## [2706] 1.703806e+00 3.204149e-01 1.025384e+00 4.878407e-02 -1.947013e+00
## [2711] 4.908649e-02 3.688666e-01 2.146294e-01 1.394285e-01 2.492513e-01
## [2716] -3.145016e-01 1.605287e+00 -8.522119e-01 2.016073e-01 1.844314e+00
## [2721] 9.566433e-01 7.624330e-01 1.356771e+00 -1.396725e-01 7.541567e-01
## [2726] 4.086364e-01 9.940923e-01 1.719956e+00 1.843406e+00 1.706430e+00
## [2731] 2.042058e+00 6.134453e-01 3.366802e+00 2.897629e+00 2.685250e+00
## [2736] 9.107796e-02 1.143686e+00 1.408958e+00 -1.248916e+00 -4.257377e-02
## [2741] -4.394397e-01 1.225106e-01 8.583582e-01 1.045997e+00 -6.848372e-01
## [2746] 1.816159e+00 5.044470e-01 3.249785e-01 -2.642180e+00 3.611989e-01
## [2751] 2.183883e+00 1.642359e+00 6.090481e-01 8.979207e-01 1.399140e+00
## [2756] 1.516467e+00 2.383763e-01 5.255403e-01 8.115662e-01 5.766401e-01
## [2761] 3.247308e-01 4.396560e-01 1.223014e+00 7.251364e-01 -3.137202e-01
## [2766] 7.899525e-01 8.909388e-01 5.845876e-01 2.347866e-01 1.699500e+00
## [2771] 1.484070e-01 -2.111944e+00 4.530494e-01 -5.635257e-01 5.650633e-02
## [2776] 5.802549e-01 6.325429e-01 1.742702e+00 -3.801840e-02 4.639189e-01
## [2781] 5.459909e-01 5.250449e-01 3.863773e-01 -1.913429e-01 6.644807e-01
## [2786] 2.868269e-01 7.592531e-01 -6.215323e-01 -5.781073e-01 9.840527e-01
## [2791] -2.126838e-01 -1.471749e-01 1.420456e+00 1.750463e-01 3.101242e-01
## [2796] -2.073570e+00 -2.609301e+00 -1.284831e-01 5.475992e-01 3.799157e-01
## [2801] 9.864040e-01 5.620083e-01 8.479841e-01 2.210703e-01 -8.386996e-02
## [2806] 1.510921e+00 1.708611e+00 1.453112e+00 -4.826170e-01 -1.073088e+00
## [2811] -2.759259e+00 -5.800386e-01 7.491767e-01 2.856136e-01 8.825209e-01
## [2816] -3.053525e-01 8.981433e-01 2.099410e+00 1.347247e+00 -4.478325e-01
## [2821] 2.001996e-01 2.040872e-01 1.794050e+00 1.634511e+00 -6.166791e-01
## [2826] 4.101950e-01 6.657190e-01 9.876925e-01 1.364244e-01 2.533059e-01
## [2831] 2.739038e-01 1.108882e+00 5.706738e-01 1.208159e+00 -1.200688e+00
## [2836] 1.503494e+00 -5.567162e-01 1.016399e-01 1.022972e+00 2.042172e+00
## [2841] 1.151589e+00 5.599046e-01 1.027775e+00 -4.305014e-01 5.502735e-01
## [2846] 2.513746e-01 -2.680913e-01 2.074543e-01 5.299232e-01 3.356004e-01
## [2851] 6.045429e-01 -8.655190e-01 -1.380391e-01 2.501363e-01 7.137244e-01
## [2856] -7.736434e-01 4.479654e-02 3.103719e-01 -1.872578e-01 1.678668e-01
## [2861] 1.578074e-02 -2.400411e-01 4.601316e-01 -5.370588e-01 -1.895840e-01
## [2866] -1.304076e+00 8.487941e-02 -1.139264e+00 -1.670218e+00 -2.932948e-01
## [2871] -2.750482e-01 -3.777431e-01 -1.116474e-01 -8.712626e-01 -6.064052e-01
## [2876] -3.136701e-01 -4.232610e-02 -1.578317e+00 -4.856504e-02 -4.381762e-01
## [2881] -2.563063e-01 -1.063991e-01 5.658708e-01 9.243966e-03 4.063325e-01
## [2886] 4.446816e-01 -4.544557e-02 4.130668e-01 5.554071e-02 -1.057688e+00
## [2891] 3.065093e-01 8.912616e-01 -1.069696e+00 -1.178382e+00 -2.343701e+00
## [2896] 7.282165e-02 -7.015368e-02 -2.673483e-01 -9.132516e-01 -1.574282e+00
## [2901] -1.617682e+00 -2.349653e-01 2.441450e-01 2.161199e-01 1.031613e+00
## [2906] 7.288265e-01 3.717957e-01 -1.307939e+00 3.514204e-01 1.964123e-01
## [2911] 5.239068e-01 3.926915e-01 9.511994e-01 -4.566817e-02 9.908372e-01
## [2916] 1.769776e-01 5.930306e-01 7.312029e-01 7.165962e-01 1.249183e+00
## [2921] 1.145820e+00 4.356211e-01 5.471540e-01 1.024656e+00 8.514515e-01
## [2926] 9.540099e-02 2.815787e-01 -2.416745e-01 -2.961176e-03 2.645704e-01
## [2931] 5.258381e-01 7.596620e-02 -1.064645e+00 -2.773172e+00 2.276322e-01
## [2936] 1.724222e-01 7.046136e-01 8.797382e-02 3.727864e-01 5.536826e-02
## [2941] 3.257216e-01 -1.020032e+00 -1.531411e-01 -1.488334e-01 -4.897714e-01
## [2946] 4.536200e-01 -7.193991e-01 -6.267635e-02 -1.749774e-01 1.429110e-01
## [2951] -2.733897e-01 -1.536364e-01 -7.525000e-01 -3.011922e-01 -4.833349e-01
## [2956] -1.937193e-01 -5.658270e-01 -9.704071e-02 -3.170372e-01 -2.522463e-01
## [2961] -4.528331e-01 -1.608410e-01 -2.306576e-01 -2.549036e-02 -2.882189e-01
## [2966] -2.786630e-01 6.704970e-01 -1.177194e+00 1.001207e-02 -1.173108e+00
## [2971] -1.754443e+00 3.348574e-01 -9.533206e-02 3.699146e-01 3.180467e-01
## [2976] 6.426945e-01 -1.966162e-01 1.412275e-01 4.807296e-01 -2.464776e-01
## [2981] -1.018187e-01 -8.216129e-02 3.801885e-01 3.372948e-02 -2.918338e-01
## [2986] 1.038125e+00 7.216219e-01 -7.589728e-02 7.125111e-01 3.221569e-01
## [2991] -1.656190e-01 -2.964399e+00 -1.222900e+00 5.827837e-01 4.952936e-01
## [2996] 2.068837e-01 9.689991e-01 9.711391e-02 4.803129e-01 2.657397e-01
## [3001] 5.029142e-01 7.251486e-01 3.691754e-01 4.187621e-01 3.881983e-01
## [3006] 1.350851e+00 -4.630968e-01 -1.677095e-01 4.919625e-02 6.436304e-01
## [3011] 1.672502e-01 8.907784e-01 7.357653e-01 4.826455e-01 2.707062e-01
## [3016] 7.224538e-01 2.909952e-01 2.563688e-01 -1.813630e-01 4.207121e-01
## [3021] -7.675130e-01 5.082226e-01 -5.489382e-01 6.110150e-01 -2.175772e-01
## [3026] -3.293986e-01 -1.673879e-01 -3.896021e-01 9.343391e-02 6.635978e-01
## [3031] 1.852676e-01 5.737954e-01 7.400683e-01 1.824915e-01 1.136941e+00
## [3036] 8.472449e-01 -5.728869e-01 -7.229335e-01 -4.762424e-02 1.076900e-01
## [3041] 3.678483e-01 -3.932414e-01 -5.043586e-01 7.250877e-01 -1.668032e+00
## [3046] -1.510667e+00 1.225156e+00 4.549965e-01 1.020235e+00 9.739453e-01
## [3051] 1.567435e+00 2.953387e-01 1.086752e+00 3.126316e-01 4.520614e-01
## [3056] 1.004248e+00 4.879539e-01 -2.366001e-01 1.021521e+00 -5.210083e-01
## [3061] 1.170241e+00 -3.133685e-02 1.124334e+00 9.367257e-01 8.415743e-01
## [3066] 8.256084e-01 -1.258451e-01 2.158250e+00 -1.451127e+00 -1.563886e-01
## [3071] 7.557124e-01 1.364846e+00 2.704049e-01 3.399183e-01 9.755940e-01
## [3076] 2.637084e-01 1.436993e+00 2.973090e-01 5.677421e-01 1.252019e+00
## [3081] 1.373130e+00 -1.290484e+00 6.442533e-01 4.216972e-01 9.975724e-01
## [3086] 1.727093e+00 2.007520e+00 1.157894e+00 -1.517844e-01 1.148926e+00
## [3091] 1.413346e+00 4.939258e-01 6.662316e-01 1.345522e+00 9.196934e-01
## [3096] 4.227433e-01 1.402005e+00 9.821684e-01 1.665220e+00 3.801544e-01
## [3101] -3.879127e-01 -5.745559e-01 5.445383e-01 -5.938394e-01 9.427179e-01
## [3106] -7.060809e-02 7.760015e-01 -1.477828e-01 9.838985e-01 1.000287e-01
## [3111] 5.624337e-01 9.572583e-02 9.446679e-01 -2.891829e-01 7.760218e-01
## [3116] 3.515202e-01 4.286950e-01 -6.530984e-01 7.980001e-01 1.386361e+00
## [3121] 5.544509e-01 -3.982688e-01 5.607851e-01 5.521386e-01 6.316256e-01
## [3126] 7.360870e-01 8.784722e-01 8.615214e-01 8.172634e-01 -2.404114e-01
## [3131] 8.347067e-01 1.026292e+00 1.473257e+00 -1.409909e+00 -1.357359e-01
## [3136] 3.506971e-01 1.118350e+00 9.713409e-02 2.216180e+00 -1.073676e+00
## [3141] 9.930823e-01 1.326086e+00 1.825741e+00 8.806348e-01 2.113927e+00
## [3146] 1.202837e+00 4.728345e-01 5.925500e-01 1.958541e-01 1.097354e+00
## [3151] 7.465859e-01 -2.898237e-03 1.492032e+00 4.731380e-01 1.139346e+00
## [3156] 8.300636e-01 5.626723e-01 1.095840e+00 1.178712e+00 1.108660e+00
## [3161] 5.336013e-01 1.304081e+00 5.261954e-02 -1.269702e+00 -1.469399e-01
## [3166] 3.303070e-01 1.882837e-01 1.247554e+00 -3.423609e-01 6.723945e-01
## [3171] 7.688932e-01 5.625322e-02 3.853201e-01 -3.857654e-01 -5.229438e-01
## [3176] -5.623105e-01 -1.218449e-02 -1.472088e+00 2.673201e-01 -3.181354e-01
## [3181] -3.019848e-01 1.522478e-01 7.431540e-01 3.776481e-01 3.091093e-01
## [3186] -1.846073e+00 -1.485008e+00 2.618693e-01 4.842413e-01 3.208185e-01
## [3191] -1.844256e+00 -1.725045e+00 -5.505006e-01 3.482742e-01 2.415439e-02
## [3196] -1.173001e+00 -1.863738e+00 1.379382e+00 1.229991e+00 1.801314e+00
## [3201] -4.545060e-01 -8.053755e-01 -2.297782e+00 -2.230556e+00 -2.190078e+00
## [3206] -1.157456e+00 6.355513e-01 -1.674373e+00 -3.619434e-01 1.236249e+00
## [3211] 5.789235e-01 7.737392e-01 6.154642e-01 -1.636116e+00 -8.129458e-01
## [3216] -1.701930e+00 6.063797e-01 -4.521842e-01 -4.073667e-01 6.939958e-01
## [3221] 7.918075e-01 -2.498994e-01 -9.051049e-01 3.370702e-01 7.296292e-01
## [3226] -9.707519e-02 -3.072343e-01 9.833931e-01 3.227362e-01 -2.150752e-01
## [3231] 7.135783e-01 1.324976e+00 5.552021e-01 -8.418151e-01 -2.055020e+00
## [3236] 5.012997e-01 2.994194e-01 6.517015e-01 1.083627e+00 4.148948e-01
## [3241] 6.680543e-01 1.165691e+00 7.383091e-01 1.915478e+00 -9.386168e-01
## [3246] -1.379223e+00 -3.376172e-01 -6.992294e-02 2.440028e-01 5.782166e-01
## [3251] -1.706618e-01 1.537185e-02 -7.941149e-02 5.924493e-01 -1.410273e-02
## [3256] 2.559133e-01 5.740781e-01 9.518986e-01 7.124677e-01 -8.052157e-02
## [3261] 4.779823e-01 5.291595e-01 1.236450e+00 2.982072e-01 4.757617e-01
## [3266] 1.088269e+00 7.594047e-01 3.060804e-01 7.335638e-01 -2.563601e-01
## [3271] 1.369044e-01 -1.185764e-01 5.432906e-01 5.040251e-01 4.846444e-01
## [3276] -1.482528e-01 8.518662e-01 7.280124e-01 -9.156029e-01 -1.926662e-01
## [3281] -3.262109e-01 -8.425640e-02 -4.479452e-01 5.966884e-01 -5.861729e-02
## [3286] 1.532567e-01 1.291318e-01 -2.385943e-01 -3.268162e-01 -4.811546e-01
## [3291] -4.448157e-01 -1.817646e-01 -5.330377e-01 -6.296381e-01 2.556109e-01
## [3296] -6.440727e-01 -2.244626e-01 -6.380161e-01 -3.925286e-01 1.330689e-01
## [3301] 6.096093e-01 -8.808793e-01 -2.389983e-01 -3.608333e-01 -3.731888e-02
## [3306] -1.875183e-01 -1.267521e-01 -2.695830e-01 3.212828e-02 8.201715e-01
## [3311] 5.391527e-01 7.192304e-01 -2.346576e-01 -2.783033e-02 2.879702e-02
## [3316] 2.311830e-01 2.062509e-01 8.088239e-02 -5.023933e-02 -2.520609e-02
## [3321] 4.464515e-02 -9.242838e-01 2.741839e-01 7.620295e-01 5.964866e-01
## [3326] 5.487419e-01 3.353537e-01 3.273365e-02 3.701780e-01 -2.322369e+00
## [3331] -9.844187e-01 6.147153e-01 -6.833406e-01 -4.591155e-01 2.827314e-01
## [3336] -2.702700e-03 3.672254e-01 5.615095e-01 -3.400560e-01 6.137098e-01
## [3341] 4.613714e-01 6.695697e-01 3.994989e-01 2.254630e-01 9.483073e-01
## [3346] -1.384321e-01 1.050765e-01 7.494189e-01 4.749639e-01 4.430526e-01
## [3351] -4.381832e-01 -1.733802e-01 7.048597e-01 -1.334946e-03 -4.185375e-01
## [3356] -1.400332e-02 7.920079e-01 4.786439e-01 5.415015e-01 5.914944e-02
## [3361] -2.116459e-01 1.898718e-01 -1.783151e+00 -1.906934e-01 -1.178419e-01
## [3366] 3.209564e-01 2.893668e-01 -7.665076e-01 -1.122782e+00 -1.106474e+00
## [3371] -1.071547e+00 -3.330716e-02 -7.448711e-01 -9.654568e-01 4.416882e-02
## [3376] -4.072572e-01 -8.523490e-01 2.082108e-01 -8.999248e-01 -6.906396e-01
## [3381] 1.488571e+00 1.188457e+00 -1.022180e-01 1.381455e+00 3.738812e-01
## [3386] -1.003360e+00 -2.383236e+00 4.224420e-01 -3.606869e-01 -5.432879e-01
## [3391] -1.851639e+00 -3.613911e-01 -2.023643e+00 -7.535582e-01 -1.728236e+00
## [3396] -2.295820e-01 -1.204300e+00 4.929610e-01 -5.200028e-01 -1.129458e+00
## [3401] 2.457521e-01 -4.764457e-02 -3.274283e-01 -1.092198e+00 -3.919346e-01
## [3406] 2.746875e-01 4.237082e-01 1.160352e-01 -1.649351e+00 8.046153e-01
## [3411] 9.207396e-01 8.089792e-01 3.455890e-01 2.880397e-01 -1.668334e+00
## [3416] -1.680298e+00 5.494641e-01 3.914753e-01 -2.588797e-01 3.023568e-01
## [3421] 2.504782e-01 6.928891e-03 -6.493744e-02 7.503837e-01 5.398325e-01
## [3426] -3.596611e-01 6.815339e-01 5.734128e-01 -3.413628e-01 2.587827e-01
## [3431] 1.089044e+00 1.316265e+00 -8.054096e-02 3.684508e-01 5.288535e-01
## [3436] 8.658445e-01 -1.041284e+00 8.082953e-01 2.650559e-01 1.775657e-01
## [3441] 5.830444e-01 6.649046e-01 -2.404872e+00 -1.221272e+00 -6.394652e-01
## [3446] 8.075911e-01 -2.156678e-01 4.689717e-01 -7.026614e-02 6.136692e-01
## [3451] -3.869275e-01 -1.225070e-01 -2.106204e-02 2.860287e-01 -3.968202e-02
## [3456] 5.451409e-01 2.268104e-01 5.348458e-01 8.059627e-01 -5.629337e-01
## [3461] 4.825845e-01 8.894513e-01 -9.095360e-01 6.382813e-01 -1.346686e+00
## [3466] -8.160942e-01 -4.050123e-03 -1.190325e+00 -2.525658e-01 5.328145e-01
## [3471] -3.257389e-01 -6.048387e-01 -1.862317e+00 -2.311107e+00 8.278394e-01
## [3476] 1.285771e+00 8.226972e-01 1.339249e+00 1.214706e+00 6.651536e-01
## [3481] 1.106274e+00 4.818812e-01 6.432780e-01 1.767987e+00 5.185018e-01
## [3486] 1.536924e+00 1.246696e+00 7.509323e-01 1.395135e+00 1.317373e+00
## [3491] 1.079302e+00 1.395756e+00 1.021972e+00 9.799759e-01 -1.786345e-01
## [3496] -2.251828e-01 1.315586e+00 -1.510014e+00 1.291224e+00 1.390738e+00
## [3501] 1.348710e+00 1.083777e+00 9.869829e-01 2.504820e+00 8.873456e-01
## [3506] 1.053030e+00 1.321615e+00 1.091717e+00 9.228008e-01 7.521755e-01
## [3511] 5.472607e-01 1.011213e-01 9.768998e-01 -1.037990e+00 2.905459e-01
## [3516] 7.524086e-01 8.337127e-01 6.894377e-01 -2.390413e-01 3.974551e-01
## [3521] 1.993022e+00 7.200297e-01 1.841274e+00 2.115623e+00 2.772038e+00
## [3526] 2.547003e+00 2.312443e+00 1.627177e+00 1.330766e+00 1.350808e+00
## [3531] -4.227798e-01 5.850927e-01 1.500024e+00 1.228596e+00 1.416234e+00
## [3536] 4.617150e-01 1.137597e+00 1.389806e+00 1.625382e-01 -1.747354e-01
## [3541] 1.726213e-01 9.641749e-01 2.769982e-01 1.247084e+00 7.477008e-01
## [3546] 1.297797e+00 2.248097e-01 1.193763e+00 1.811661e+00 9.854608e-01
## [3551] 1.378712e+00 1.030533e+00 1.050031e+00 1.096269e+00 1.324846e+00
## [3556] 1.424205e+00 1.722294e+00 -2.248854e+00 1.189907e+00 1.410411e+00
## [3561] 1.241122e+00 8.475665e-01 6.802677e-01 8.279683e-02 1.532890e+00
## [3566] 8.083359e-01 9.873179e-01 1.330240e+00 4.832684e-01 1.962263e-01
## [3571] 1.564358e+00 1.366133e+00 1.826205e+00 2.010516e+00 1.991916e+00
## [3576] 1.602664e+00 1.725444e+00 1.758682e+00 1.482599e+00 7.011797e-01
## [3581] 1.478960e+00 2.863503e-01 5.871069e-01 1.339229e+00 1.349866e+00
## [3586] 1.174906e+00 1.300320e+00 2.387682e+00 1.047802e+00 1.010220e+00
## [3591] 1.280332e+00 9.962656e-01 1.329898e+00 -1.311535e-01 1.682513e+00
## [3596] 1.313933e+00 4.027963e-01 -2.149636e-01 3.778218e-01 1.284395e+00
## [3601] 1.067448e+00 1.179872e+00 -1.128815e+00 4.050882e-01 3.948134e-01
## [3606] 4.593604e-01 6.309621e-01 5.471315e-01 1.785915e-01 3.625399e-01
## [3611] 1.223043e+00 8.961274e-01 1.294388e+00 8.100049e-01 -2.046482e-01
## [3616] 1.170623e+00 9.832553e-01 9.221073e-01 7.677172e-01 1.426787e-01
## [3621] 1.193827e+00 1.294991e+00 1.413790e+00 1.001936e+00 3.801950e-01
## [3626] 1.712153e+00 4.057721e-01 4.170727e-01 9.945763e-01 7.184522e-01
## [3631] 6.580524e-02 4.603049e-01 2.697210e-01 -1.635623e-02 9.633083e-01
## [3636] 9.413298e-01 9.197341e-01 9.316983e-01 7.837034e-01 6.020876e-01
## [3641] 1.188478e+00 9.420137e-01 -1.958996e+00 1.928193e-01 -1.229814e-01
## [3646] 7.021862e-02 -1.124321e-01 -7.797995e-01 -2.028353e+00 -5.284458e-01
## [3651] -4.616219e-01 -3.764647e-01 -1.722946e+00 1.040421e-01 -1.555158e-01
## [3656] -2.357321e-01 9.610271e-02 4.062950e-01 4.238831e-01 6.667628e-02
## [3661] -1.637478e+00 -3.034107e-01 -3.379334e-01 1.513673e-01 -3.993503e-01
## [3666] -1.803121e-01 -1.903965e+00 3.769463e-01 -3.125615e-01 3.940364e-01
## [3671] -9.647576e-02 7.782150e-01 5.998011e-02 4.849891e-01 -9.875113e-01
## [3676] -7.296853e-02 -9.335676e-01 7.140328e-01 4.092935e-01 -2.366644e-01
## [3681] -3.824615e-01 -2.826688e-01 -7.543125e-03 1.512119e-01 2.916337e-01
## [3686] -8.274355e-01 -5.068492e-01 -6.267623e-01 -5.756078e-03 2.437646e-01
## [3691] -2.204750e-01 -7.713162e-01 3.742586e-01 1.553758e-01 -4.790227e-01
## [3696] -9.287822e-01 -9.048087e-01 -1.981804e+00 -3.547904e-01 -3.856931e-01
## [3701] 6.369703e-01 1.391359e+00 -1.384927e+00 1.433122e+00 1.711768e-01
## [3706] 1.206487e+00 3.572145e-01 -2.509115e-01 1.159939e+00 2.711249e-01
## [3711] 5.395544e-01 5.465614e-01 -8.828555e-01 -7.082995e-01 -7.273319e-01
## [3716] -4.282328e-01 -3.794631e-01 -6.312370e-01 -2.924412e-01 -5.157210e-01
## [3721] -5.253379e-01 -1.838850e+00 2.945544e-01 -1.368562e+00 3.273313e-01
## [3726] 1.925870e-01 3.353142e-01 -4.560990e-01 8.980526e-03 -1.201565e+00
## [3731] 3.772193e-01 4.533885e-01 5.824621e-01 8.083563e-01 1.169597e+00
## [3736] 6.440130e-01 1.109394e+00 6.087637e-01 5.678031e-01 1.013599e+00
## [3741] 7.394860e-01 7.810085e-01 -6.523739e-01 8.143282e-01 4.035005e-01
## [3746] 6.257147e-01 7.417983e-01 8.778697e-01 9.353986e-01 -2.505549e-01
## [3751] 8.389403e-01 3.525664e-01 3.366209e-01 4.151227e-01 2.956807e-01
## [3756] 1.600191e-03 -8.812642e-01 -3.147193e-01 1.548172e+00 9.922640e-01
## [3761] 4.137956e-01 7.274812e-01 9.480467e-01 -1.400809e-01 -1.134780e-01
## [3766] 1.626054e-01 -2.605284e-01 1.296887e-01 -3.988714e-01 1.186910e+00
## [3771] 2.288418e-01 7.707134e-01 4.224218e-01 -1.240877e+00 1.766415e-01
## [3776] 1.628322e+00 1.776077e+00 6.283891e-01 -9.032221e-01 1.367068e-01
## [3781] 9.612877e-02 5.398529e-01 -2.028952e+00 -1.724255e+00 7.515587e-02
## [3786] -1.041477e-01 -1.107158e+00 3.329409e-01 5.861218e-01 5.384107e-02
## [3791] -4.714216e-01 9.178520e-02 1.013278e+00 2.029227e-01 5.941047e-01
## [3796] -1.622769e+00 NA -2.133735e+00 -4.265780e-02 -7.258888e-01
## [3801] -8.154644e-02 -1.044896e-01 -7.788677e-02 1.220477e-01 -3.310066e-01
## [3806] 2.870139e-01 -4.132088e-01 -5.239841e-01 -2.437978e-02 -3.386575e-03
## [3811] 1.459964e-01 -5.000354e-01 2.351353e-01 -3.909088e-01 4.786915e-02
## [3816] -2.761319e-01 -2.329404e-01 -1.051532e-01 -6.856731e-01 -1.999830e-01
## [3821] 4.247543e-01 -9.397985e-01 -6.251075e-01 6.885926e-01 4.310682e-01
## [3826] -4.660726e-01 1.243600e-01 -1.906730e-01 -8.520611e-02 -2.433912e-02
## [3831] 9.974777e-02 -1.587211e-01 -8.596684e-01 -1.417499e-01 3.445835e-01
## [3836] -4.448391e-01 -8.862305e-01 -6.440693e-01 -1.786885e-01 3.978095e-01
## [3841] -8.852384e-02 -2.652342e-01 1.858499e-01 -3.200886e-01 -9.394972e-01
## [3846] 5.983331e-02 5.583171e-02 -8.019903e-02 -1.431843e+00 -7.292269e-01
## [3851] 1.202091e+00 -3.463089e-01 -2.365601e+00 -1.202249e+00 -1.896066e-01
## [3856] -1.000172e-02 1.963793e-02 4.307466e-01 1.673112e-01 -4.694037e-02
## [3861] 9.912486e-02 -9.714996e-02 -2.461910e-01 -3.023928e-01 -6.177881e-01
## [3866] -1.866714e-01 2.258659e-01 1.569957e-01 5.522913e-02 1.287442e-01
## [3871] -2.371823e-01 3.859981e-02 3.329612e-01 5.212328e-01 2.381314e-01
## [3876] -4.551138e-01 -1.129418e+00 1.826338e-01 6.842490e-01 -7.371895e-01
## [3881] 8.539413e-01 -1.440825e-01 -1.879782e-01 -1.888919e+00 -1.024313e+00
## [3886] -1.607118e-01 -1.008327e+00 -1.078202e+00 -7.545230e-01 3.089719e-01
## [3891] -7.268944e-01 -4.760868e-01 -1.009332e+00 -1.510599e-01 -6.257507e-01
## [3896] -4.670984e-01 -3.736160e-01 -1.224867e-01 -4.800273e-01 -1.161322e-01
## [3901] 2.164950e-01 -3.433331e-01 3.707834e-01 6.452385e-01 1.160555e-01
## [3906] 8.913102e-02 4.384688e-01 -1.763465e+00 -1.789103e+00 2.957010e-01
## [3911] 1.426990e-01 8.935342e-01 -4.657509e-01 -1.809030e+00 -1.802966e-01
## [3916] 5.486687e-02 -1.743450e-01 -2.329297e-02 4.271072e-01 6.090853e-01
## [3921] 2.371463e-01 8.582442e-01 3.055595e-02 2.930265e-01 -1.237935e-01
## [3926] -1.119122e+00 2.788144e-02 -9.918124e-02 -2.864677e-01 -4.102127e-01
## [3931] -1.466429e+00 -2.638065e+00 4.200688e-01 1.110281e-01 -2.425517e-01
## [3936] 2.238550e-01 2.501362e-01 -1.237731e-01 -7.784717e-01 -7.615005e-01
## [3941] 3.119917e-02 3.556304e-02 -3.734942e-02 -1.790102e-01 -5.845295e-01
## [3946] -4.597993e-01 -2.861258e-01 3.146426e-01 -4.764693e-01 -1.693951e+00
## [3951] -8.190498e-01 -2.116256e-01 1.270142e-01 5.528428e-01 -1.277795e+00
## [3956] 1.024223e-01 -1.434526e-02 4.437162e-01 -2.495697e-01 -1.372237e-02
## [3961] 8.943230e-02 -7.206009e-01 -5.742140e-01 -9.720721e-01 -2.322027e+00
## [3966] -2.519023e-01 1.849054e-01 2.461549e-01 -1.429169e+00 5.747805e-01
## [3971] 3.372642e-01 -4.713669e-03 -6.390826e-01 -7.977958e-01 1.702463e-01
## [3976] 7.244071e-02 -3.397141e-01 6.768078e-01 4.949516e-01 2.877181e-01
## [3981] -4.704364e-01 4.130102e-01 -1.643511e-01 3.319761e-01 -2.874528e-01
## [3986] -6.560742e-01 -9.594036e-01 -1.367835e-01 -7.947388e-01 5.178744e-01
## [3991] -8.683351e-01 -9.245167e-01 2.936697e-01 1.250032e-01 -6.251481e-01
## [3996] -9.471382e-01 -2.977886e-01 -9.943720e-01 -2.591810e-01 -9.777630e-01
## [4001] -4.865005e-02 1.003910e-01 2.121921e-01 -6.300943e-01 4.386685e-01
## [4006] -3.583137e-01 -5.226773e-01 1.077103e-01 3.615547e-01 -1.411067e-01
## [4011] 5.491425e-01 2.417504e-01 4.779397e-01 2.510808e-01 -2.852720e+00
## [4016] -7.993299e-01 -3.471936e-01 -1.556016e-02 -1.019605e-01 -8.576707e-01
## [4021] -2.807581e-01 -5.056379e-01 -1.016348e+00 -1.159224e+00 -1.557417e+00
## [4026] -9.770398e-01 -5.278242e-01 -7.212573e-01 -1.654988e+00 1.458825e-01
## [4031] -7.140173e-01 -1.614748e+00 -1.622376e+00 -8.611353e-01 -7.620007e-02
## [4036] -7.216459e-01 -8.598921e-01 -5.158764e-01 -1.279137e+00 -5.673655e-01
## [4041] -8.568159e-01 -1.362741e+00 -6.367995e-01 -1.111977e+00 -4.051459e-01
## [4046] -8.451789e-01 -9.099050e-01 -1.058266e+00 -8.219824e-01 -1.285367e+00
## [4051] -8.795462e-01 -8.057930e-01 -4.383477e-01 -1.607663e+00 -8.135769e-01
## [4056] -2.090702e+00 -8.256025e-01 -9.821042e-01 -1.434707e+00 -1.041222e+00
## [4061] -5.494667e-01 -7.209148e-01 -9.956061e-01 -1.711542e+00 -4.102881e-01
## [4066] -7.338728e-01 -1.007166e+00 -7.394352e-01 -8.679869e-01 -1.107114e+00
## [4071] -1.389946e+00 -8.242816e-01 -5.564737e-01 -9.826279e-02 -8.283678e-01
## [4076] -1.556407e+00 -5.317550e-01 -2.506784e-01 -7.790542e-01 -1.412987e+00
## [4081] -1.819662e+00 -3.652161e-01 -2.939951e-01 -3.128722e-01 -1.361724e-01
## [4086] 2.374805e-02 -2.049583e+00 5.604431e-01 3.877953e-01 3.921389e-01
## [4091] 3.356154e-01 4.061344e-01 1.143865e-01 -4.431700e-01 6.046808e-01
## [4096] 6.070134e-01 6.542878e-01 5.760670e-01 5.657718e-01 5.065535e-01
## [4101] 1.610325e+00 6.456413e-01 8.970922e-01 2.214411e-01 4.420269e-01
## [4106] 6.588920e-01 -1.206975e+00 1.301968e+00 9.496750e-01 3.269283e-01
## [4111] 1.021883e+00 5.641434e-01 6.096675e-01 1.674489e+00 4.962380e-01
## [4116] 5.654502e-01 8.545033e-01 9.210002e-01 -2.589000e-01 1.038452e+00
## [4121] 3.884183e-01 -1.681987e+00 -2.169340e-01 8.365875e-01 -1.211799e-01
## [4126] -3.184399e-01 -1.015342e-01 2.411072e-01 -1.986966e-01 -1.042994e+00
## [4131] 1.273155e-01 3.682916e-02 -6.484536e-01 9.449083e-01 1.470222e-01
## [4136] 1.193732e-01 -5.855350e-01 4.569871e-01 -1.721942e+00 3.768163e-01
## [4141] 1.373093e-01 5.940641e-01 5.594783e-01 5.647663e-01 8.155943e-01
## [4146] 6.912060e-01 -2.888206e-01 1.957694e-02 4.852590e-01 -5.107335e-01
## [4151] -4.042611e-01 3.295825e-01 -6.424614e-01 -6.630719e-01 -6.084985e-01
## [4156] -1.364822e-01 1.093387e-01 3.292406e-01 5.637812e-01 6.159814e-01
## [4161] 4.197066e-01 3.881373e-01 -2.393873e+00 -7.754959e-01 3.216200e-01
## [4166] 4.516991e-01 -4.728230e-02 -1.287125e+00 -4.517961e-01 -1.235227e+00
## [4171] -2.734981e-01 -4.850751e-01 -7.959646e-02 2.035050e-01 -2.708235e-01
## [4176] -8.053962e-01 -4.122236e-01 -1.860079e-01 1.486099e-01 -2.412449e-01
## [4181] -1.481044e-01 -1.551566e+00 -2.006669e-01 -1.467455e+00 4.613307e-01
## [4186] -1.007342e+00 -3.410209e-01 -5.815536e-01 -1.142025e-01 -7.901143e-01
## [4191] -1.182021e+00 -1.290765e+00 -3.985904e-01 -1.033965e+00 -9.055344e-01
## [4196] -4.857590e-01 -9.504966e-01 -1.713597e+00 -2.158689e+00 -1.225937e+00
## [4201] 2.281579e-01 2.081701e-01 -3.290567e-01 -8.979341e-01 -8.556464e-01
## [4206] -6.420787e-01 -5.326508e-01 -1.913163e-01 -3.187615e-01 -5.026489e-01
## [4211] 3.851851e-02 -3.064554e-01 -5.629541e-01 -8.742867e-01 -1.818340e+00
## [4216] -2.621094e+00 2.530917e-01 4.457069e-01 -2.360763e-03 5.657921e-01
## [4221] -3.839111e-01 -5.169660e-01 -7.238779e-01 4.344265e-01 -9.551211e-01
## [4226] -1.301422e+00 -1.227880e-01 8.575603e-01 1.016555e+00 -5.525776e-01
## [4231] 2.537553e-01 6.180737e-01 6.167060e-01 3.482431e-01 -7.578611e-01
## [4236] 4.547155e-01 1.632893e-01 1.403258e-01 3.515406e-01 -8.403645e-01
## [4241] 2.667452e-01 1.403054e-01 5.345648e-01 -5.605605e-01 3.199713e-01
## [4246] -1.214609e-01 2.434804e-01 2.128556e-01 1.180461e-01 1.140445e-01
## [4251] 7.952850e-01 6.233414e-01 -6.680587e-01 2.558272e-01 -1.001048e+00
## [4256] -1.193643e+00 -7.255807e-02 -7.049567e-01 -7.611788e-01 -4.341816e-01
## [4261] 4.753262e-01 -1.104819e-01 5.295576e-01 2.651168e-01 -1.939704e-01
## [4266] -5.256531e-01 -1.813112e+00 2.774026e-01 9.261466e-03 -8.173807e-01
## [4271] -3.127489e-01 -4.484784e-01 -1.539924e+00 -7.555285e-01 -1.735127e+00
## [4276] 3.136647e-01 1.026680e+00 1.023215e+00 3.438681e-01 1.176874e+00
## [4281] 8.014114e-01 5.481613e-01 4.057511e-01 2.759104e-01 5.874695e-01
## [4286] 7.893859e-01 6.665521e-01 8.909575e-02 1.552204e-01 -1.692966e-01
## [4291] -3.937817e-02 4.370423e-01 -5.646827e-02 7.531539e-01 4.004535e-01
## [4296] 1.645277e+00 -6.533456e-01 3.340957e-01 9.492286e-01 3.051037e-01
## [4301] 1.054040e+00 1.317995e+00 4.284354e-01 8.071293e-01 1.174186e+00
## [4306] -1.610405e-02 5.154256e-01 5.124272e-01 7.532633e-01 7.786034e-01
## [4311] 2.021889e-01 1.812917e-01 1.062176e-01 9.864390e-01 7.393730e-01
## [4316] 1.878325e-01 5.084187e-01 -1.652881e-01 -5.194187e-01 1.069530e+00
## [4321] 1.433744e+00 1.491696e+00 1.542175e+00 2.179558e+00 1.323557e+00
## [4326] 1.882416e+00 7.701202e-01 7.048503e-01 3.515202e-02 6.236238e-01
## [4331] 3.591569e-01 1.550192e+00 1.178546e-01 1.028777e+00 5.593639e-01
## [4336] 8.683131e-01 4.624143e-01 6.709491e-01 6.715707e-01 1.182902e+00
## [4341] -4.828759e-01 1.017451e+00 -1.692189e-01 1.013210e+00 -2.372543e-01
## [4346] 5.547339e-01 6.793547e-01 4.430710e-01 6.280985e-01 1.550348e+00
## [4351] 7.446022e-02 6.091437e-01 9.000704e-01 1.243387e+00 8.814265e-01
## [4356] 9.157160e-01 -9.557360e-01 -5.924664e-01 -3.539428e-02 -2.640062e-01
## [4361] -5.788003e-01 -6.689922e-01 -1.212671e+00 -2.346820e+00 -7.534907e-01
## [4366] 1.716792e-01 -1.668573e-01 -9.708631e-01 4.965495e-01 2.000021e-01
## [4371] -4.149292e-01 -5.102220e-01 9.804879e-01 1.061817e+00 -6.121488e-01
## [4376] -2.253843e-01 -9.576172e-01 -4.511495e-01 -5.730315e-01 -3.366055e-02
## [4381] -5.113600e-01 -3.052773e-01 -1.069721e+00 1.383306e-01 -2.276534e+00
## [4386] -8.772538e-01 8.029898e-02 -6.977853e-01 -3.148163e-02 -6.728188e-02
## [4391] -2.921065e-01 -6.987761e-01 -4.274572e-01 -5.941249e-01 -5.871930e-01
## [4396] -1.164938e+00 -6.970674e-01 -1.486108e-01 -1.575445e+00 -2.522724e+00
## [4401] -2.592293e+00 -2.634034e+00 -2.613164e+00 -9.494971e-01 -1.265232e+00
## [4406] -8.292986e-01 -9.768293e-01 -1.346808e+00 -8.331109e-01 -4.907370e-01
## [4411] -1.417120e+00 -1.852558e+00 -1.759964e+00 -8.561606e-01 1.107507e-01
## [4416] -3.561545e-01 -9.883666e-01 -5.053939e-01 -5.454516e-01 -4.118347e-01
## [4421] -9.262249e-01 -5.524918e-02 -8.117060e-02 -2.270930e-01 -2.143362e+00
## [4426] -1.034168e+00 -1.193261e+00 -1.138299e+00 -6.580003e-01 -8.169102e-02
## [4431] -5.905351e-01 -6.855803e-01 -3.446422e-01 1.010915e+00 -2.618273e-01
## [4436] -8.422467e-01 -2.004447e+00 -2.689194e+00 -2.396575e+00 -1.582034e-01
## [4441] -5.980811e-01 3.404811e-01 -4.413462e-01 -3.976667e-02 -8.552620e-01
## [4446] 1.113598e-01 -1.255455e-01 -2.198534e-01 1.653035e-01 -5.949273e-01
## [4451] -7.469861e-01 1.041197e-01 1.211639e-01 -6.623728e-01 -1.431794e-01
## [4456] -4.727927e-01 -4.297091e-01 -4.677363e-02 1.239293e-01 -1.017274e-01
## [4461] 7.739734e-01 -6.757192e-01 4.444378e-01 8.299055e-01 1.249073e+00
## [4466] 1.272612e+00 -2.829796e-01 1.092649e+00 2.373015e-01 4.284037e-01
## [4471] 1.007328e-01 -1.511188e-01 -1.144946e+00 -3.516366e-01 6.797114e-01
## [4476] 1.074393e+00 3.421965e-02 -3.985733e-01 3.750815e-01 2.736572e-01
## [4481] -7.559354e-01 -7.509169e-01 7.839105e-02 -5.851549e-01 -1.563786e-02
## [4486] 2.304877e-02 -2.642025e-02 -5.393600e-02 4.836683e-01 8.253071e-01
## [4491] 4.497671e-01 1.852134e+00 1.213960e+00 -4.387362e-01 -4.258560e-01
## [4496] 1.033997e+00 5.460175e-01 -9.083511e-01 6.953887e-01 5.165911e-01
## [4501] 5.904220e-01 2.250428e-01 -7.219155e-02 1.138622e+00 -7.523155e-01
## [4506] -3.280516e-01 -7.505284e-01 -1.366846e-01 -4.673080e-01 -6.893446e-01
## [4511] -5.423043e-01 2.866928e-01 -1.018958e+00 9.113968e-01 -5.078216e-02
## [4516] 1.139238e-01 5.035555e-01 -3.519474e-01 6.208586e-01 4.308900e-01
## [4521] 4.466910e-01 -2.611463e+00 3.535515e-01 1.305950e+00 4.030772e-01
## [4526] 6.110353e-01 -4.028458e-02 5.314873e-01 -4.221972e-01 6.349636e-01
## [4531] 7.929930e-01 6.978009e-01 1.036160e+00 2.218237e-01 1.994831e-01
## [4536] 9.476640e-01 3.495296e-01 7.351018e-01 1.179831e+00 4.150617e-01
## [4541] 5.080429e-02 6.130260e-01 -2.262643e-01 9.433206e-01 5.608054e-01
## [4546] 6.642207e-01 7.447335e-01 3.349112e-01 1.036823e+00 1.402648e+00
## [4551] 1.617282e+00 8.837806e-01 8.142266e-01 9.246802e-01 7.031500e-01
## [4556] 4.951919e-01 7.723011e-01 1.403352e+00 6.093256e-01 -2.397211e+00
## [4561] 6.111977e-02 3.671441e-01 5.630973e-01 -6.141691e-01 5.776138e-02
## [4566] -9.725157e-02 -4.431903e-01 -1.647947e-01 -2.648923e-01 5.950085e-01
## [4571] -1.069576e+00 -1.437514e+00 9.319589e-01 3.022958e-01 2.733604e-01
## [4576] 4.732746e-01 -3.008254e-01 -6.983009e-01 4.234272e-01 2.969468e-01
## [4581] -9.498127e-01 -3.069363e-02 2.182821e-02 -2.945116e-01 -4.774748e-01
## [4586] -7.632102e-01 3.830895e-01 -1.016350e+00 -4.614887e-01 -2.905100e-01
## [4591] -5.416799e-01 2.224059e-01 -8.430390e-01 -6.966928e-01 -1.971462e-02
## [4596] -1.393618e+00 4.752246e-01 4.915119e-01 7.638134e-02 1.919434e-02
## [4601] 5.803902e-01 1.934705e-01 4.939055e-01 -2.138078e+00 5.797470e-01
## [4606] 4.763316e-01 1.058856e-02 6.483362e-01 8.162782e-01 -4.351871e-01
## [4611] -1.390347e-01 -1.632421e+00 -1.919504e+00 1.314576e+00 1.608355e+00
## [4616] 1.234104e+00 7.919876e-01 1.931008e+00 9.224086e-01 8.835200e-01
## [4621] 6.745564e-01 5.482623e-02 1.097369e+00 5.255358e-01 2.076370e+00
## [4626] 2.320582e+00 1.292357e+00 -7.595301e-01 -1.953585e-01 1.305286e+00
## [4631] 1.083756e+00 1.678552e+00 1.110961e+00 6.995310e-01 9.107254e-01
## [4636] 5.920531e-01 -6.567377e-01 -1.373289e+00 1.054438e+00 1.521508e+00
## [4641] 9.929072e-01 2.159940e+00 -4.212120e-01 -1.372625e+00 4.290572e-01
## [4646] 5.544712e-01 -7.362453e-02 5.867447e-01 9.463370e-01 6.978619e-01
## [4651] 1.186849e+00 1.566429e+00 3.821653e-01 3.199306e-01 -1.401622e-01
## [4656] 1.263080e+00 1.701858e+00 2.980336e-01 7.204835e-01 5.827634e-01
## [4661] 1.289963e+00 -1.687959e+00 1.499574e-01 -6.262519e-02 1.075109e+00
## [4666] 1.080319e-01 1.139937e+00 1.405103e+00 1.453000e+00 1.849257e-01
## [4671] 1.081041e+00 1.075411e+00 1.092040e+00 -8.755933e-03 9.217247e-01
## [4676] -1.201304e+00 4.503517e-01 4.862442e-01 8.937948e-01 4.354117e-01
## [4681] 5.066145e-01 4.879336e-01 3.505351e-01 5.997143e-01 1.336151e+00
## [4686] 1.098032e+00 -2.519953e+00 -8.404711e-01 -8.369288e-01 -2.298129e-01
## [4691] -1.058033e+00 -1.447587e+00 -4.574580e-01 -1.104814e+00 -6.576650e-01
## [4696] -9.292483e-01 -2.671009e-01 -1.148799e+00 -3.332256e-01 -1.145800e+00
## [4701] -1.603421e+00 -8.364626e-01 -7.089987e-01 -6.461833e-01 -4.496740e-01
## [4706] -5.043170e-01 -9.926077e-01 -6.807060e-01 3.602130e-01 -1.084383e+00
## [4711] -7.425891e-01 -1.316115e+00 -1.173860e+00 1.855792e-01 5.742326e-01
## [4716] -7.912034e-01 4.009292e-02 -5.029961e-01 -7.958701e-02 -4.077558e-01
## [4721] -5.577168e-01 -1.880960e-01 4.306569e-01 -1.024333e+00 -8.678314e-01
## [4726] -1.273031e+00 -1.426612e+00 -1.156816e+00 -4.536048e-01 -1.133775e+00
## [4731] -4.643872e-01 2.705033e-01 -1.240140e+00 5.125049e-01 -2.472138e-01
## [4736] 3.562821e-01 -8.091022e-01 -9.578978e-01 -1.076102e+00 -5.469344e-01
## [4741] -2.952383e-01 -2.194425e+00 -2.446743e+00 1.304335e-02 8.166685e-01
## [4746] 7.594156e-01 4.873660e-01 9.395800e-01 -3.998942e-01 1.793995e+00
## [4751] 3.134316e-01 2.476177e-01 6.115206e-01 1.102344e+00 1.034153e+00
## [4756] 9.245560e-01 4.724974e-01 8.804623e-01 6.837976e-01 6.414909e-01
## [4761] 1.618389e-01 2.860712e-01 1.060348e+00 3.783131e-01 -3.104954e-01
## [4766] 1.075294e+00 2.466076e-01 8.206771e-01 9.312522e-01 5.171809e-01
## [4771] 1.002162e+00 5.617408e-01 1.044081e+00 7.671995e-01 -2.509939e-02
## [4776] 7.400723e-01 2.408191e-04 2.056218e-01 3.061915e-01 9.297299e-01
## [4781] 5.618962e-01 1.227741e+00 -1.308952e+00 -9.995369e-01 6.231576e-01
## [4786] -1.589027e-01 -5.620362e-01 -4.713165e-01 -5.962481e-01 -3.372341e-01
## [4791] 4.886869e-01 1.376491e+00 1.042248e+00 1.433666e+00 -7.345720e-01
## [4796] 1.038705e+00 1.886890e+00 4.089050e-01 6.220159e-02 -2.899866e-01
## [4801] 1.028746e+00 -9.670885e-02 4.335777e-01 6.813430e-01 6.573697e-01
## [4806] 9.026121e-02 4.795454e-02 1.593843e-01 1.037819e+00 1.173766e+00
## [4811] 2.880596e-01 -4.153844e-01 8.286576e-02 1.090606e-01 -9.184565e-02
## [4816] 3.834094e-01 -7.713244e-02 7.014633e-01 -3.730777e-01 -1.236807e-01
## [4821] -2.644133e-01 -1.976148e+00 1.106207e+00 1.509955e+00 2.390982e+00
## [4826] 8.293174e-01 1.181272e+00 1.593736e+00 1.525800e+00 1.341108e+00
## [4831] 2.097883e-02 1.413994e+00 1.304095e+00 1.217048e+00 -6.260877e-01
## [4836] 8.600168e-01 8.655378e-01 -3.816056e-01 -6.757516e-01 -2.467984e+00
## [4841] 9.070314e-01 9.099283e-01 -5.265122e-01 -7.877798e-01 1.372030e+00
## [4846] -7.693107e-01 -1.141468e+00 -2.689690e+00 -2.745345e+00 -6.889474e-01
## [4851] -1.611443e+00 -1.469604e-02 -1.989926e-01 -1.231596e-01 8.634090e-01
## [4856] -1.070920e-01 6.342767e-01 -1.197925e-01 6.400874e-02 -2.054542e-01
## [4861] 3.241132e-01 5.426738e-01 -1.168776e+00 -4.569182e-01 -7.721825e-01
## [4866] 3.876157e-01 -5.071889e-02 -4.679852e-01 6.270972e-01 -2.337771e-01
## [4871] -6.694875e-01 -9.547704e-01 -8.693314e-01 8.185230e-01 -2.873035e-01
## [4876] -1.644074e+00 -2.511880e+00 -2.141208e+00 7.928494e-01 3.002955e-01
## [4881] 5.474267e-01 5.771963e-02 -2.505878e-01 -9.466002e-01 3.012611e-01
## [4886] -9.894688e-02 5.677770e-01 4.065802e-01 1.148667e+00 4.147253e-01
## [4891] 9.516446e-01 7.497551e-02 1.224004e+00 7.194429e-01 4.171268e-01
## [4896] 7.482362e-01 9.375082e-01 1.168127e+00 9.057209e-01 -1.545519e-01
## [4901] 4.186380e-01 -3.893387e-02 2.029240e-01 9.032191e-01 6.256362e-01
## [4906] -1.299323e+00 -9.045139e-01 -3.112686e-01 -8.038725e-01 -1.195950e-01
## [4911] -7.064509e-01 -1.413976e+00 2.991073e-01 5.015752e-01 -6.349759e-01
## [4916] 1.087293e+00 -3.496679e-01 7.785405e-01 7.260048e-01 -1.345150e+00
## [4921] -1.092497e+00 -1.651278e+00 -1.295238e+00 -1.216533e+00 -1.129213e+00
## [4926] -6.973402e-01 -7.520548e-01 -2.078348e+00 -1.617459e+00 -8.988676e-01
## [4931] -1.243173e+00 -9.458823e-01 -1.602580e+00 -1.674550e+00 -9.010214e-01
## [4936] -1.407514e+00 -1.560591e+00 -9.708381e-01 -6.860505e-01 -3.993318e-01
## [4941] -3.899734e-01 -3.249347e-01 -6.577526e-01 6.205854e-01 1.713241e+00
## [4946] 2.045761e+00 -6.946910e-01 7.098368e-01 4.296047e-01 -8.671667e-02
## [4951] -3.220629e-01 -5.027697e-01 6.325931e-01 1.460054e-01 1.137847e+00
## [4956] 9.730608e-01 2.120877e+00 1.756691e+00 1.163349e+00 1.828217e+00
## [4961] -2.139524e+00 8.302830e-01 -1.562026e+00 1.616347e-01 6.414052e-01
## [4966] 1.208893e+00 -4.675276e-01 1.276181e-01 2.367344e-01 -6.932309e-01
## [4971] 1.281228e-01 2.864985e-01 -1.191675e+00 1.477598e+00 -6.041002e-01
## [4976] 1.279956e+00 8.885728e-02 1.163269e+00 2.033237e-01 1.623456e+00
## [4981] -6.203514e-01 6.866273e-01 2.228050e-01 -1.040407e-01 3.501919e-01
## [4986] 7.406304e-01 -2.816958e-01 -1.268895e+00 -1.494699e+00 3.381803e-01
## [4991] 8.409652e-01 5.157348e-01 4.570886e-01 3.451452e-01 3.250580e-01
## [4996] -4.509098e-02 3.021054e-02 -1.518865e-01 -1.410855e-01 -4.176630e-01
## [5001] -8.334710e-02 -7.601544e-01 -1.280805e+00 -1.244466e+00 -1.099111e+00
## [5006] -7.985107e-01 -6.548109e-02 -3.040037e-01 -7.089764e-01 -1.437869e+00
## [5011] 3.296000e-01 -3.689089e-01 -1.790392e-01 2.336060e-01 -1.558190e+00
## [5016] 4.164090e-01 -3.714325e-01 -1.055548e-01 -7.011046e-01 -5.494912e-01
## [5021] 3.152666e-01 4.605204e-01 1.098869e+00 9.052645e-01 1.109468e+00
## [5026] 1.687554e+00 1.277735e+00 2.489488e-01 1.595092e+00 1.585301e+00
## [5031] 3.627088e-01 7.815113e-01 9.679485e-01 1.710226e-01 1.113504e+00
## [5036] 5.167442e-01 1.113202e+00 5.553038e-01 -3.045084e-01 1.428541e+00
## [5041] -8.045678e-01 1.072725e+00 1.152367e+00 1.063034e+00 2.057805e+00
## [5046] 1.078277e+00 -1.121018e+00 -1.014067e-02 1.273137e-01 -1.717505e+00
## [5051] -1.334837e-01 -2.455119e-01 -1.930014e-01 3.600859e-01 -2.215218e-01
## [5056] 2.352819e-01 1.587561e-01 -9.317821e-02 4.347056e-01 1.244419e-01
## [5061] -1.584646e-01 2.664767e-01 9.387466e-01 1.420086e+00 2.969453e-02
## [5066] 3.715982e-01 5.532958e-01 1.049885e+00 7.684390e-01 6.105843e-01
## [5071] 8.159240e-01 5.505462e-01 -3.343795e-02 5.781512e-01 1.059961e+00
## [5076] 5.606979e-01 1.436791e-01 -5.068046e-01 -1.459802e+00 -1.780340e+00
## [5081] 5.799239e-02 -3.235239e-01 -4.232969e-01 -2.198382e-01 -4.861816e-01
## [5086] 4.090068e-01 -7.217755e-01 -4.124524e-01 1.558321e-02 -6.641891e-01
## [5091] -5.428025e-01 -7.702512e-01 -3.539756e-01 -1.322075e+00 -8.765360e-01
## [5096] -9.045860e-01 -1.136145e+00 -9.199609e-01 -9.540274e-01 -1.331211e+00
## [5101] -6.723593e-01 -5.202733e-01 -1.017853e+00 -6.694624e-01 -3.719494e-01
## [5106] -1.962210e+00 -2.285867e+00 -8.090957e-01 -5.271800e-01 -6.510184e-01
## [5111] -2.008988e-01 -4.756099e-01 -7.704989e-01 3.284209e-01 1.434315e-01
## [5116] -7.620810e-01 -6.877341e-01 -5.596131e-01 2.719726e-01 -8.979020e-01
## [5121] -1.343441e+00 -2.957545e-02 1.853703e-01 4.667907e-01 -2.061972e-01
## [5126] 2.115645e-01 -1.469408e+00 6.616258e-02 -2.930220e-01 -3.107732e-01
## [5131] -2.529391e-01 -1.476451e-01 -9.133822e-01 -2.070002e-01 -9.535975e-02
## [5136] 6.402531e-02 -4.496611e-01 -1.708636e-01 -7.482435e-01 8.219428e-02
## [5141] -2.264815e-01 1.394280e-01 1.866684e-01 5.654984e-01 6.070852e-01
## [5146] 3.019426e-01 4.196391e-01 -5.665506e-01 9.727929e-01 4.421487e-01
## [5151] 1.256999e-01 -4.418890e-01 2.579317e-01 5.741788e-01 2.859523e-02
## [5156] -2.278363e-02 1.906046e-01 5.723616e-01 -1.923638e-01 -7.291657e-01
## [5161] 1.998908e-01 6.816803e-01 6.411023e-01 -4.301798e-01 3.313160e-01
## [5166] -2.488905e-01 -4.362360e-01 -8.817877e-01 2.747892e-01 -6.891931e-01
## [5171] 3.441353e-01 -3.081426e-01 5.073558e-01 -2.203247e-01 -5.632193e-01
## [5176] -3.667888e-01 -5.562543e-01 -3.502347e-01 -7.999248e-01 -7.767081e-01
## [5181] -4.336112e-01 -3.853618e-01 -6.774839e-01 -9.231732e-01 -5.659447e-01
## [5186] -4.979105e-01 -4.013106e-01 -5.749285e-01 -5.617051e-01 -6.396313e-01
## [5191] -2.688765e-01 -9.424917e-02 2.965929e-01 -1.060595e-01 1.231768e-01
## [5196] 3.240486e-01 3.495432e-02 -3.388698e-02 1.455853e-01 -3.995946e-01
## [5201] -4.063578e-01 -3.513448e-01 -7.265410e-01 -8.559467e-01 -3.000670e-01
## [5206] -8.417144e-01 -1.672455e+00 -5.131528e-01 7.486042e-01 8.764959e-01
## [5211] -1.174658e-01 5.716152e-02 -9.638523e-01 -5.364701e-01 -1.294775e-01
## [5216] -1.672700e+00 -4.051189e-02 4.809806e-01 5.486275e-01 8.143693e-01
## [5221] -1.444278e+00 -3.515906e-01 1.160296e+00 3.554051e-02 3.009398e-01
## [5226] -4.770343e-01 4.286368e-01 -1.309651e+00 1.144495e+00 2.386683e-01
## [5231] -5.377519e-01 4.986523e-02 5.409990e-01 -3.210447e-01 -2.349794e+00
## [5236] 5.016907e-01 5.718299e-02 2.896136e-01 5.412320e-01 -3.921879e-01
## [5241] 2.904682e-01 6.554589e-01 -4.183828e-01 2.067190e-02 2.288960e-01
## [5246] 7.646673e-01 3.928249e-01 1.702444e-01 -1.779810e+00 -1.656044e+00
## [5251] -4.754027e-01 -6.004897e-01 -2.325005e-01 4.454478e-01 4.801259e-01
## [5256] -2.357720e-02 -6.105728e-01 9.641350e-02 -1.667448e+00 5.114171e-01
## [5261] 3.697062e-01 2.457070e-01 6.794323e-01 -2.140578e-01 2.816283e-01
## [5266] 1.366376e+00 5.806179e-01 -3.171139e-01 -1.978224e-01 1.175864e+00
## [5271] -6.887230e-01 6.482188e-01 1.576290e-01 1.256345e+00 -1.554418e+00
## [5276] -2.193095e-01 1.107828e+00 8.131262e-01 6.990783e-02 1.114556e+00
## [5281] 5.961858e-01 4.275031e-01 5.665263e-01 8.263949e-01 7.241159e-01
## [5286] 1.828916e-01 2.778988e-01 5.827157e-01 4.262599e-01 4.470018e-01
## [5291] 6.104646e-01 6.088330e-01 -1.083139e+00 -1.445257e-01 5.249646e-02
## [5296] 1.760370e-01 -4.077246e-01 -4.221086e-01 -7.287324e-01 2.497412e-01
## [5301] -3.707612e-01 6.074398e-01 3.300543e-01 -1.163255e+00 5.011300e-01
## [5306] -4.707899e-02 -1.133059e-01 -2.495971e-01 2.091881e-01 1.002177e+00
## [5311] -9.245413e-01 4.244066e-01 3.992533e-01 9.280194e-02 1.376628e-01
## [5316] -1.315273e-01 -1.869850e-01 -6.217299e-01 -1.180511e+00 2.461264e-01
## [5321] -2.183773e-01 2.301038e-02 -2.793057e-01 -1.062466e+00 -6.881542e-01
## [5326] -4.484501e-01 -1.016639e+00 -1.077345e+00 -1.942527e+00 -2.336744e+00
## [5331] -9.912134e-01 -8.373936e-01 -1.088858e+00 -3.872489e-01 -6.024675e-01
## [5336] -7.253652e-01 -1.680294e+00 -1.326630e+00 -1.204996e+00 -9.904954e-01
## [5341] -8.894840e-01 -8.422217e-01 -6.106878e-01 -7.258607e-01 -1.060560e+00
## [5346] -1.963645e+00 -1.347279e+00 -1.332844e+00 -6.454723e-01 -7.094952e-01
## [5351] -6.183376e-01 -9.602413e-01 -1.337895e+00 -1.121488e+00 -5.430250e-01
## [5356] -7.663887e-01 -6.361139e-01 -8.997831e-01 -2.430551e+00 -1.110207e+00
## [5361] -5.054085e-01 -3.863824e-01 -5.101244e-01 3.559456e-01 5.994143e-01
## [5366] 9.881363e-01 5.793270e-01 6.343401e-01 6.473233e-02 9.348402e-01
## [5371] -2.306199e-01 1.355056e+00 7.487054e-01 1.000956e+00 1.120369e+00
## [5376] 1.052940e+00 -2.279411e+00 -3.182365e-01 1.224842e+00 6.257609e-01
## [5381] 1.463939e-01 9.534132e-01 1.063202e-01 4.948405e-01 3.745192e-01
## [5386] 5.006953e-01 5.020077e-01 -1.079768e-01 1.800911e+00 7.301329e-01
## [5391] 7.377032e-01 1.778869e-01 -8.274122e-02 -1.198842e+00 3.113304e-01
## [5396] 1.342843e+00 -5.340466e-01 6.968218e-01 1.346837e-01 -3.297869e-02
## [5401] 3.068886e-01 3.516049e-01 7.870622e-01 -2.531295e-01 -6.493212e-01
## [5406] -2.324360e-01 -9.525811e-02 4.774782e-01 2.749915e-01 6.043603e-01
## [5411] 8.915971e-02 3.140548e-01 9.661314e-01 6.010296e-01 1.755646e-01
## [5416] 6.450394e-01 -3.456927e-01 -1.271556e-01 4.989789e-01 -8.728422e-02
## [5421] 6.242462e-01 8.545921e-01 7.794924e-01 2.530878e-01 1.549265e+00
## [5426] -3.734508e-01 9.626995e-01 2.084716e-01 8.547944e-01 1.320736e+00
## [5431] 7.374003e-01 1.128242e+00 9.232321e-01 1.081709e+00 -2.315282e-01
## [5436] 1.349605e+00 1.131371e+00 -9.313858e-02 1.522113e+00 1.776281e+00
## [5441] 1.328004e+00 2.073551e+00 -2.863748e-02 -6.244898e-01 7.490088e-01
## [5446] 1.707238e+00 1.677561e+00 1.206068e+00 1.533822e+00 -1.042419e-01
## [5451] 1.356469e+00 1.027872e-01 1.976245e+00 2.628624e+00 1.640112e+00
## [5456] 2.688380e+00 4.645582e-01 1.058594e+00 1.922544e+00 3.156039e+00
## [5461] 2.727747e+00 1.892262e+00 1.438635e+00 8.738722e-01 2.615198e+00
## [5466] 1.210573e-01 1.271477e+00 1.469988e-01 1.466797e+00 9.562043e-02
## [5471] 1.775373e+00 6.504907e-01 9.291877e-01 2.488821e+00 6.191996e-01
## [5476] 7.042925e-01 9.151576e-01 -1.870131e-01 -5.832048e-01 2.452413e-03
## [5481] 3.424880e-02 -3.091510e-01 2.861594e+00 -1.067316e+00 6.617953e-01
## [5486] -2.002370e-01 -1.060250e+00 -1.087100e+00 -1.136763e+00 -1.436557e+00
## [5491] -9.935287e-01 -4.559570e-02 -1.574442e+00 -1.674676e+00 -9.246874e-01
## [5496] -1.458461e+00 -1.927633e+00 -1.767945e+00 -7.988147e-01 -1.872318e+00
## [5501] -4.104957e-01 -3.723402e-01 -1.917574e-01 1.410371e+00 2.495281e+00
## [5506] 2.663650e+00 1.874295e+00 2.147137e+00 1.860365e+00 1.919011e+00
## [5511] 2.395653e+00 2.478928e+00 2.918828e+00 4.832324e-01 2.050840e+00
## [5516] 2.278562e+00 2.976061e+00 1.284398e+00 1.635065e+00 1.140053e+00
## [5521] 2.205849e-01 1.882975e+00 1.330225e+00 1.045774e+00 6.730003e-01
## [5526] 2.175299e+00 1.505962e+00 2.449757e+00 -2.361275e+00 4.601611e-01
## [5531] 6.139292e-01 3.669868e-01 6.545266e-01 4.871329e-01 4.616751e-02
## [5536] 1.176718e+00 3.200500e-01 4.513671e-01 6.795101e-01 -2.956909e+00
## [5541] -1.315424e-01 5.296726e-01 8.793028e-02 2.786758e-01 4.859992e-01
## [5546] 7.359083e-01 -8.285029e-02 2.402222e-01 -6.148685e-02 -2.348774e-01
## [5551] 5.580431e-01 7.667333e-01 -5.194762e-02 1.472034e-01 -6.234153e-02
## [5556] 4.890754e-01 3.367834e-01 2.522478e-01 8.486590e-01 -1.346185e-01
## [5561] 3.153198e-02 8.882780e-01 -6.491817e-01 -6.183568e-01 4.694990e-01
## [5566] 1.473441e+00 2.017687e-01 -2.623931e-01 -8.114014e-01 6.352244e-02
## [5571] -4.997646e-01 4.601211e-02 -2.976151e-01 -2.911521e-01 2.137942e-01
## [5576] -1.538064e-01 -1.015308e-02 7.122868e-02 1.983358e-01 8.674125e-01
## [5581] 1.663487e+00 6.546043e-01 -2.116897e+00 3.988996e-01 -3.223815e-03
## [5586] -3.517143e-01 1.076149e+00 8.132039e-01 6.358825e-01 1.858900e-01
## [5591] 7.282859e-02 1.457219e+00 1.018041e+00 8.518128e-01 -7.270212e-01
## [5596] -4.245433e-02 8.566760e-01 4.910955e-01 9.567018e-01 1.223500e+00
## [5601] 3.122120e-02 -8.908030e-02 3.055699e-01 1.064745e+00 7.752942e-01
## [5606] 3.612230e-01 5.100962e-01 -9.551324e-01 -4.367161e-01 -2.263814e+00
## [5611] -2.022749e-01 2.617381e-01 5.568038e-01 8.285639e-01 -8.182739e-02
## [5616] -1.038464e-01 -1.713489e-01 2.943332e-01 3.288853e-02 -2.418881e-01
## [5621] -4.544707e-01 6.715266e-02 -7.804827e-01 -3.539905e-01 2.344717e-01
## [5626] -2.289388e-01 2.338285e-01 -4.432107e-01 -1.567508e-01 -4.893780e-01
## [5631] -3.380450e-01 8.213329e-02 -3.137138e-01 -4.830845e-01 2.338082e-01
## [5636] -1.738204e-02 1.313577e-01 4.856416e-01 2.507591e-01 8.742134e-02
## [5641] -6.791323e-02 -5.875663e-01 -5.928543e-01 -1.630850e-01 -8.822256e-02
## [5646] -1.876972e-01 -1.324365e+00 2.710888e-01 -1.087191e+00 -4.599586e-02
## [5651] 2.610746e-01 -8.094181e-01 -1.295430e+00 -7.771446e-01 -7.302527e-01
## [5656] -2.023156e-01 -9.587604e-01 -2.196975e+00 -2.468393e+00 5.601418e-01
## [5661] 5.371783e-01 -1.081696e-01 2.736820e-01 1.892286e-01 -4.155007e-01
## [5666] 3.140197e-01 -3.523011e-01 -4.857183e-01 -3.260606e-01 5.847540e-01
## [5671] -8.177633e-01 1.852473e-01 -2.086294e-01 5.782235e-02 -7.778285e-01
## [5676] -3.926185e-01 4.952123e-01 1.133607e-01 -4.092275e-01 -7.136031e-01
## [5681] -1.197986e+00 4.986520e-01 -1.167075e-02 1.369674e-01 -1.066218e+00
## [5686] 1.459017e-02 -1.547243e+00 -9.794523e-01 6.276443e-01 2.234724e-01
## [5691] 3.754689e-01 2.354569e-01 -1.945463e+00 -1.136094e+00 4.297208e-01
## [5696] 8.182688e-01 -3.260402e-01 -5.349631e-01 -1.084879e+00 -3.184196e-01
## [5701] -1.627024e-01 -8.453513e-01 -3.232201e-02 3.220466e-02 -4.019082e-01
## [5706] -5.055606e-03 -5.758627e-01 -7.724355e-02 5.681687e-02 -3.609882e-01
## [5711] -6.041549e-01 3.952400e-02 -1.197204e-02 -8.426564e-01 -9.527885e-01
## [5716] -5.075930e-03 -6.767051e-01 -4.508110e-01 -7.003118e-01 -1.198122e-01
## [5721] 1.825728e-01 -6.564161e-01 -8.330249e-01 -5.958707e-01 2.304498e-01
## [5726] -1.102172e+00 -2.096349e-01 -6.088200e-01 -6.859711e-02 -1.513361e+00
## [5731] -7.792742e-02 -9.461734e-01 -1.780656e-01 -9.357160e-02 -4.930783e-01
## [5736] 3.721512e-01 -1.931830e+00 -1.626107e+00 -1.541935e+00 -2.087849e+00
## [5741] -2.500842e+00 1.208709e+00 3.953573e-01 9.180929e-01 1.173020e+00
## [5746] 8.779300e-01 1.605181e-01 1.490965e+00 3.195062e-01 -1.335225e+00
## [5751] 7.657233e-01 1.389883e+00 8.488921e-01 8.042545e-01 1.696229e-01
## [5756] 1.200381e+00 1.014032e+00 1.220268e+00 3.385704e-01 1.250938e+00
## [5761] 1.077392e+00 7.407716e-01 8.753977e-01 1.727747e+00 4.084388e-01
## [5766] 5.774641e-01 1.420398e+00 1.224121e+00 9.181706e-01 1.344733e+00
## [5771] 1.005037e+00 1.414012e+00 8.609953e-01 9.313616e-01 5.388552e-01
## [5776] 1.109227e+00 1.478304e+00 1.162860e+00 8.513467e-01 -6.210843e-02
## [5781] -6.311593e-01 -6.496479e-01 -7.067596e-01 -2.955633e-01 6.976102e-01
## [5786] -6.873245e-01 6.089106e-01 3.129971e-01 1.197693e+00 -4.915051e-02
## [5791] 6.714153e-01 -1.894946e-01 1.179159e+00 1.076848e+00 1.498392e+00
## [5796] 4.193307e-01 3.892745e-02 9.997078e-01 3.886611e-01 4.305015e-01
## [5801] 6.681061e-01 1.444604e+00 2.748226e-01 5.672256e-01 1.311454e+00
## [5806] 1.355516e+00 1.593120e+00 1.536256e+00 1.322003e+00 1.178583e+00
## [5811] 8.116040e-01 1.616861e+00 9.096050e-02 1.472819e+00 1.007259e+00
## [5816] 1.139430e+00 8.356503e-02 1.830470e-01 7.944044e-01 7.624140e-01
## [5821] 8.809602e-01 4.989254e-01 7.484001e-01 8.457383e-01 8.054199e-01
## [5826] -2.450674e+00 6.062689e-01 6.233908e-01 -1.633774e-01 4.991585e-01
## [5831] 1.921977e-01 -1.690176e-01 5.542676e-01 5.216557e-01 5.678154e-01
## [5836] -1.161299e-01 1.387118e+00 8.617499e-02 5.651277e-01 -2.217958e-01
## [5841] 7.861543e-01 9.707158e-01 1.358437e+00 1.275493e-01 1.208243e+00
## [5846] 1.019129e+00 4.739108e-04 1.085020e+00 1.158696e+00 9.904476e-01
## [5851] -1.467163e+00 6.722382e-01 -1.587543e+00 8.583536e-01 2.519370e-01
## [5856] -1.200366e+00 -2.201642e-01 3.072793e-01 1.115457e+00 2.759881e-01
## [5861] 1.973953e-02 -7.969990e-01 -3.363017e-01 1.942874e-02 -1.695818e+00
## [5866] 1.822700e-01 -1.372435e+00 -1.052004e+00 1.681783e-01 3.733262e-01
## [5871] 1.280396e+00 -3.101069e-01 -3.018568e-01 -1.171373e+00 7.774698e-01
## [5876] 1.306125e+00 1.054428e+00 -2.874543e-01 -4.138304e-01 7.340436e-01
## [5881] 7.992676e-01 5.850150e-01 5.540345e-01 3.138200e-01 1.329166e+00
## [5886] 7.482447e-01 -5.331441e-02 -4.254675e-01 8.135147e-01 4.933947e-01
## [5891] 7.124012e-01 7.440031e-01 3.863301e-01 -3.429979e-01 5.748541e-01
## [5896] -2.387305e-01 1.651022e-01 -8.744865e-02 7.139550e-01 5.109509e-01
## [5901] 1.441272e-01 -7.037470e-01 3.184502e-01 1.012977e+00 5.330136e-01
## [5906] 4.283578e-01 -1.988399e+00 2.032770e+00 1.523122e+00 1.629210e+00
## [5911] 1.271577e+00 7.029791e-01 7.644105e-02 8.991067e-01 -1.279796e+00
## [5916] -4.651052e-01 -5.169883e-01 -3.722396e-01 1.310238e+00 3.153677e-01
## [5921] 9.582582e-01 1.742527e-01 2.294266e-02 1.417134e+00 -3.698171e-01
## [5926] 5.443614e-02 6.056727e-01 -3.989888e-01 1.893938e-01 7.987719e-01
## [5931] -3.713313e-01 1.454846e-01 2.912428e-01 2.247233e-01 4.807083e-01
## [5936] 6.967213e-01 1.847507e-01 -1.864646e+00 4.897927e-01 7.841355e-01
## [5941] -2.441457e-01 -6.224709e-01 2.025161e-01 6.792104e-03 8.870946e-01
## [5946] 2.837731e-01 3.154683e-01 2.546575e-02 2.822589e-01 -2.474770e-01
## [5951] 5.024102e-01 5.302311e-02 -5.787640e-01 -4.421920e-01 -2.421273e-01
## [5956] 3.828556e-02 -1.033299e+00 -6.425586e-01 6.816373e-02 -3.498311e-01
## [5961] -4.581403e-01 -6.477069e-01 -8.865319e-01 2.093793e-01 8.633318e-02
## [5966] 8.234016e-01 1.791988e-01 -1.104967e+00 -8.690690e-01 -3.946486e-01
## [5971] 6.673473e-01 8.520685e-01 1.148026e+00 -2.716019e-01 3.674531e-01
## [5976] 1.118956e+00 1.378070e+00 8.764555e-02 -2.492941e-01 3.668472e-01
## [5981] -5.444445e-01 2.669160e-01 7.180196e-01 1.176492e+00 -3.401408e-01
## [5986] 2.054432e-01 1.169183e-01 -4.154423e-01 2.561151e-01 -1.634733e+00
## [5991] 1.015850e+00 3.791489e-01 -1.517986e+00 5.219237e-02 9.174219e-01
## [5996] 1.639732e-01 3.925011e-01 4.836510e-01 -4.707784e-01 9.120728e-01
## [6001] 3.452064e-01 1.166219e+00 6.509497e-01 -5.283480e-01 7.933350e-01
## [6006] 3.485647e-01 6.449575e-01 6.100298e-01 1.149890e-01 8.652014e-01
## [6011] 5.927166e-01 4.686297e-01 1.240384e-01 2.617585e-01 -7.954633e-01
## [6016] 8.818306e-01 6.419614e-01 5.624541e-01 3.122897e-01 8.522114e-01
## [6021] 5.817173e-01 2.900100e-01 3.961202e-01 1.259680e-01 -5.100699e-01
## [6026] 5.278481e-01 1.380832e+00 1.408420e+00 -1.075264e-01 2.161327e-01
## [6031] -1.089523e+00 -1.679333e+00 7.905587e-02 -5.938598e-01 -2.536493e-02
## [6036] -1.116448e+00 4.041030e-01 6.180127e-01 6.309417e-01 3.775409e-01
## [6041] 5.558389e-01 -1.677908e-01 7.264555e-01 1.385477e+00 8.159972e-01
## [6046] -4.372184e-01 -6.906600e-01 -3.197467e-01 -4.832845e-02 2.404437e-01
## [6051] -1.577563e-01 5.198448e-01 -1.477086e+00 1.020194e-01 -1.584845e+00
## [6056] -7.974337e-01 1.822309e-01 6.226779e-01 5.774144e-01 7.976379e-01
## [6061] 4.536288e-01 -2.672248e-01 -4.055882e-01 8.528545e-01 9.606337e-01
## [6066] 1.925260e-01 -3.906075e-01 -5.799253e-01 1.349767e-01 3.083690e-02
## [6071] 2.390962e-01 6.544298e-02 6.244686e-02 2.351149e-01 -2.159353e+00
## [6076] 5.019290e-01 4.038221e-01 7.853725e-01 8.026856e-01 4.214772e-01
## [6081] -6.011588e-01 -1.004989e+00 -3.167302e-01 -1.156041e+00 -7.665278e-01
## [6086] 2.460940e-01 -7.062837e-01 -1.224664e-01 4.207527e-01 -4.528219e-01
## [6091] -1.935235e-02 -1.475739e+00 -2.369281e+00 6.522768e-01 3.290885e-02
## [6096] 5.518849e-02 -5.293261e-02 4.460691e-01 -1.130081e+00 1.676125e-01
## [6101] -1.110476e+00 8.709973e-02 -1.018558e-01 -3.497619e-02 -8.217039e-01
## [6106] 3.218433e-02 5.714222e-01 -8.325613e-02 1.699451e-01 1.832160e-01
## [6111] -1.175406e-01 5.551347e-01 -9.290805e-02 -6.893905e-02 1.396825e-01
## [6116] 3.947931e-01 -4.199052e-01 -3.247334e-01 -1.866961e+00 -1.336011e-02
## [6121] 3.728554e-01 6.948455e-01 -4.211917e-01 -1.161728e-01 -3.610492e-01
## [6126] -3.100947e-01 1.127993e+00 9.112165e-02 -5.602186e-01 -1.198288e+00
## [6131] 9.593676e-01 9.150690e-01 2.241563e-01 3.199510e-01 -1.861311e+00
## [6136] -3.165846e-02 6.772106e-01 -9.121902e-01 -3.403776e-01 -4.501678e-01
## [6141] 9.839392e-01 1.379932e-01 4.234069e-01 -2.422504e-01 -2.675058e-01
## [6146] 2.444249e-01 -6.118162e-01 -4.733998e-03 7.572112e-03 -7.625839e-02
## [6151] -1.008706e-01 -8.895890e-01 -1.511412e-01 -1.042310e+00 -2.504333e-02
## [6156] 7.270376e-01 6.438707e-01 -3.320935e-01 -1.933800e+00 -4.019285e-01
## [6161] -2.216398e-01 -8.662835e-01 4.344062e-01 -7.392005e-01 -3.373408e-01
## [6166] -3.713240e-01 -6.417367e-01 -1.053590e+00 -8.522644e-02 -7.964281e-01
## [6171] -5.625511e-01 -2.043062e-01 -2.342268e-01 -1.540560e-01 3.143210e-01
## [6176] -2.189449e-01 -5.213299e-01 -2.083282e-01 -7.324193e-02 -9.029005e-01
## [6181] -1.161525e-01 1.073887e-01 3.269219e-03 -3.077622e-01 3.169345e-01
## [6186] -2.225843e-01 -1.102493e+00 2.918820e-02 1.912396e-01 3.562057e-01
## [6191] -4.950486e-01 -2.282752e-01 1.569754e-01 -3.982891e-01 -4.109169e-01
## [6196] -6.344377e-01 -4.670780e-01 -4.904038e-01 -3.506728e-01 6.049686e-02
## [6201] -9.138796e-01 4.137346e-01 5.225397e-01 5.840905e-01 -2.401751e-02
## [6206] -5.269802e-01 -1.300457e+00 8.504809e-02 8.356227e-01 -1.583792e-01
## [6211] -1.111803e+00 6.323907e-01 4.387295e-01 3.325583e-01 -1.051258e+00
## [6216] 1.277184e-01 4.101360e-01 -4.681039e-01 2.201546e-01 2.890249e-01
## [6221] -2.208939e+00 -4.851769e-05 -3.320528e-01 -1.042290e+00 -9.225057e-01
## [6226] -6.018223e-01 2.434398e-01 -3.004428e-01 -3.001212e-01 3.288783e-01
## [6231] 1.799389e-01 5.654299e-01 8.344006e-02 2.803988e-01 7.151548e-01
## [6236] 7.593721e-01 3.851412e-01 -2.620430e+00 -3.603044e-01 2.796946e-01
## [6241] 3.109829e-01 1.559496e-01 -1.989572e-01 -1.037283e+00 3.636064e-01
## [6246] -1.560466e-01 -2.112836e-01 6.417686e-02 3.156277e-01 8.975391e-02
## [6251] 7.678425e-02 3.349315e-01 2.913574e-01 1.666070e-01 -7.039512e-01
## [6256] -5.894519e-02 -3.227428e-01 -1.041680e-01 3.269893e-01 -1.929650e-01
## [6261] 4.058331e-01 5.498467e-01 2.970281e-01 3.128047e-02 6.233618e-01
## [6266] 3.761731e-01 -1.766299e-02 -2.328794e-01 7.644232e-02 -6.397665e-01
## [6271] -4.434916e-01 -1.723591e+00 -1.372283e+00 -4.064684e-02 1.915612e-01
## [6276] -2.419084e-01 1.726399e-01 -3.270660e-01 -1.053248e+00 -1.923217e-01
## [6281] -2.002843e-01 -4.474933e-01 1.695828e-01 -4.624332e-01 -6.806660e-01
## [6286] -3.061338e-01 -1.264477e-01 1.918624e-01 -1.173313e+00 -1.152060e+00
## [6291] 4.836103e-01 1.758630e-02 -7.690161e-02 -1.893663e-01 -3.333799e-01
## [6296] -5.266586e-01 2.580582e-01 -1.673676e-01 -4.172104e-01 1.093050e-02
## [6301] -1.464150e-01 9.241149e-03 -6.121581e-01 3.408628e-01 -7.235970e-01
## [6306] -1.806995e-01 -8.430594e-01 -3.400357e-01 -8.932689e-01 1.416325e-01
## [6311] 1.190516e-01 -4.388468e-01 2.154725e-02 -8.233932e-01 3.209158e-01
## [6316] 1.183068e-01 7.160586e-01 -2.323032e+00 4.553384e-01 5.734534e-01
## [6321] -1.171783e-01 7.674160e-01 4.314508e-01 -1.154483e-01 3.529083e-01
## [6326] -5.463247e-01 3.322774e-01 4.726720e-01 1.177881e+00 4.999384e-01
## [6331] 3.362790e-01 4.074615e-01 5.624744e-01 5.621325e-01 4.400159e-01
## [6336] -3.912508e-01 3.186238e-01 -3.177561e-01 6.519756e-01 5.235655e-01
## [6341] 6.683105e-02 -4.311855e-01 6.665533e-01 8.442284e-01 5.168486e-01
## [6346] 3.471970e-01 1.582822e-01 2.251414e-01 4.215785e-02 -8.250623e-01
## [6351] 2.693588e-01 5.937424e-01 2.587420e-01 1.340556e+00 6.120407e-01
## [6356] 4.806345e-01 -4.358304e-01 -9.880581e-01 -1.665720e+00 -1.550818e-01
## [6361] -3.769743e-01 7.444119e-01 6.253117e-01 7.115154e-01 1.323632e-01
## [6366] -1.036961e+00 4.104169e-01 2.481253e-01 -2.846646e+00 -1.331605e+00
## [6371] 2.281229e+00 3.002417e+00 3.485068e+00 2.276755e+00 2.144739e+00
## [6376] 2.108461e+00 2.118855e+00 1.687584e+00 2.499102e+00 1.811382e+00
## [6381] 2.679344e+00 2.401453e+00 1.974968e+00 2.013189e+00 2.112003e+00
## [6386] 1.689837e+00 1.629197e+00 1.580396e+00 1.914561e+00 -2.995829e+00
## [6391] 1.952860e+00 1.306047e+00 1.558132e+00 1.757484e+00 1.946863e+00
## [6396] 1.931683e+00 9.022920e-01 1.396378e+00 1.803442e+00 1.575098e+00
## [6401] 1.777791e+00 1.788496e+00 2.648949e-01 1.433433e+00 1.655314e+00
## [6406] 1.682053e+00 4.108474e-01 1.009201e+00 8.908880e-01 1.654304e+00
## [6411] 1.070540e+00 1.294917e-01 1.097978e+00 2.114334e+00 1.491728e+00
## [6416] 1.909264e+00 1.863337e+00 2.350851e+00 1.152621e+00 2.511439e+00
## [6421] 1.579774e+00 8.461267e-01 5.970405e-01 1.638892e+00 1.357691e+00
## [6426] 4.419832e-01 1.294721e+00 1.535712e+00 1.775305e+00 1.804219e+00
## [6431] 2.074559e+00 1.512360e+00 1.005581e+00 8.535222e-01 6.391917e-01
## [6436] 5.283058e-01 2.199103e+00 1.814691e+00 1.055183e-01 6.325733e-01
## [6441] 2.022138e+00 1.202479e+00 1.604524e+00 9.577896e-01 4.826899e-01
## [6446] 1.070074e+00 1.195783e+00 1.618104e+00 1.463979e+00 1.866491e+00
## [6451] -2.445966e+00 -1.168919e+00 -1.969934e+00 -7.432883e-01 -4.423563e-01
## [6456] 8.878496e-02 -8.744500e-01 -8.630890e-03 -1.228037e+00 -2.123802e-01
## [6461] -9.836583e-01 -8.348309e-01 -1.327208e+00 9.616427e-01 -4.059688e-01
## [6466] 8.870348e-01 9.022142e-01 -6.349347e-01 -7.459760e-01 4.408495e-01
## [6471] -1.126690e+00 1.743307e-01 -6.699236e-01 2.735335e-01 -1.868069e-01
## [6476] -5.189525e-01 3.823216e-01 6.260008e-01 2.164042e-01 -5.112463e-01
## [6481] 5.691363e-01 -5.408281e-01 -1.973787e+00 -5.960927e-01 -8.749938e-01
## [6486] -7.895258e-01 2.291290e-01 -2.342088e+00 -1.869520e+00 -1.753382e+00
## [6491] -1.841072e+00 -1.663828e+00 -1.209516e+00 -1.889329e+00 -1.753615e+00
## [6496] -2.384974e-01 9.484517e-01 1.585337e+00 8.298596e-01 6.317186e-01
## [6501] 1.222087e+00 7.163320e-01 9.442101e-01 4.123237e-01 1.046489e+00
## [6506] 2.697263e-01 3.493529e-01 2.453962e-01 -7.764902e-01 -2.737971e-01
## [6511] -3.168030e-01 2.832282e-01 1.778730e-01 1.602390e-01 9.182483e-01
## [6516] 7.243490e-01 -1.888708e+00 -1.891838e-01 -1.465920e+00 -3.421433e-01
## [6521] -1.924930e-01 6.311747e-01 4.882207e-01 2.891474e-01 -2.477257e+00
## [6526] -1.901590e+00 6.392419e-02 9.706734e-01 4.270076e-01 1.088269e+00
## [6531] 8.595377e-01 4.203456e-01 7.275077e-01 -5.054227e-02 1.823277e-01
## [6536] 3.763780e-03 9.931830e-01 8.804912e-02 -1.093561e+00 9.376658e-01
## [6541] -8.404020e-01 7.310978e-02 -1.985210e-01 -1.798732e+00 5.070534e-01
## [6546] 1.371062e-01 -1.824976e+00 1.022819e-01 -7.092803e-01 -5.428292e-01
## [6551] -1.311996e+00 -4.731215e-02 -1.393859e+00 -9.682937e-01 1.300406e-01
## [6556] -1.382655e+00 2.665124e-01 -1.124449e+00 -7.158412e-01 -5.940062e-01
## [6561] 1.524496e-01 -4.039349e-01 5.817496e-01 -1.745637e+00 -9.920151e-01
## [6566] 4.571887e-01 -3.231826e-01 -6.297393e-01 -9.883808e-01 -1.463508e+00
## [6571] -1.245779e+00 -1.548399e+00 -1.394667e+00 -5.403056e-01 -1.176736e+00
## [6576] -1.190565e+00 -7.260363e-01 -1.092896e-01 -3.772868e-01 1.526509e-01
## [6581] -1.305536e+00 -9.995853e-01 5.393545e-01 3.051727e-01 2.225021e-01
## [6586] -4.798422e-01 -6.024848e-01 -1.585243e+00 -8.269768e-01 -2.134604e-01
## [6591] 1.642594e-01 -4.816593e-01 -1.873166e-01 -2.631228e-01 1.630482e-01
## [6596] -4.287663e-01 -1.141810e+00 -1.853988e-01 -2.746302e-01 3.818493e-02
## [6601] -2.522212e-01 -1.358025e+00 6.157666e-01 7.003549e-01 -1.978145e-01
## [6606] 9.652823e-02 5.683249e-01 2.610617e-01 -2.108107e+00 1.729175e-01
## [6611] 2.186658e+00 8.727423e-01 1.701234e+00 4.799866e-01 -3.628467e-02
## [6616] 1.160254e+00 2.827920e-01 1.268395e+00 7.218445e-01 1.759488e+00
## [6621] 1.012896e+00 2.539999e+00 8.801444e-01 2.480506e+00 1.097097e+00
## [6626] 1.778230e+00 4.428256e-01 9.665491e-01 7.376394e-01 1.130223e+00
## [6631] -2.539549e-01 1.767906e+00 -1.492680e+00 -1.591761e+00 1.740807e-01
## [6636] 1.853453e-01 7.371690e-01 9.049277e-01 8.823735e-01 1.028246e+00
## [6641] 1.226851e+00 9.134096e-02 1.012376e+00 1.478865e-01 8.542481e-01
## [6646] 1.296445e+00 4.147504e-01 2.250929e+00 5.680246e-01 1.218706e+00
## [6651] 1.037877e+00 1.051493e+00 1.644854e-02 -2.289740e-01 6.508145e-01
## [6656] -4.759942e-02 1.106925e+00 6.820343e-01 6.079100e-01 -1.384935e+00
## [6661] -1.305264e+00 -3.016625e-01 1.158265e-01 1.266208e-01 7.223649e-01
## [6666] -5.739971e-01 -1.834203e-01 2.424865e-01 1.820283e-01 2.048051e-01
## [6671] -2.332817e-01 -2.743302e-01 -8.652713e-01 1.501612e+00 2.044746e+00
## [6676] 5.469565e-01 -4.838052e-01 3.406010e-01 1.579104e+00 -3.189434e-01
## [6681] -7.601748e-01 -9.695470e-03 -2.062503e+00 2.304789e-01 6.791626e-01
## [6686] -7.769855e-01 2.559300e-01 7.261773e-01 -3.679124e-03 3.739496e-01
## [6691] -2.340582e+00 1.210288e+00 4.315109e-01 2.229710e+00 1.817792e+00
## [6696] 9.921978e-01 1.735993e+00 -7.933008e-01 1.317093e+00 3.155950e-01
## [6701] 6.179363e-01 1.705046e+00 4.145027e-01 3.947951e-01 8.965099e-01
## [6706] 2.030965e-01 -1.479955e+00 -4.672422e-01 4.708758e-01 -4.331681e-02
## [6711] -3.904687e-01 4.475785e-01 9.250411e-02 -1.740368e-01 2.021309e-01
## [6716] -6.597090e-01 2.822464e-01 -2.771944e-02 -3.931430e-01 2.319148e-01
## [6721] -1.277957e+00 -1.044679e-01 -5.819197e-01 -1.634902e-01 -5.555531e-01
## [6726] 5.665637e-01 -9.391730e-01 -2.075006e+00 -4.159198e-01 -2.551431e-01
## [6731] 5.574780e-01 4.264852e-01 -7.995397e-01 -1.625246e-01 1.099498e+00
## [6736] -6.428482e-01 3.209185e-01 -1.721307e-01 3.079453e-01 -2.093167e-01
## [6741] 5.164546e-01 -1.515730e+00 -1.540324e+00 6.040574e-01 1.749935e+00
## [6746] 8.538856e-01 4.763676e-01 1.131573e+00 1.543209e+00 7.611212e-01
## [6751] -1.138316e-01 9.916698e-01 1.995524e+00 1.169224e+00 5.374373e-01
## [6756] 1.295400e+00 -4.734831e-01 9.504856e-01 1.072280e-01 2.042320e-01
## [6761] 1.097455e+00 -1.651358e+00 -6.914138e-01 -8.244532e-01 1.278644e+00
## [6766] -6.054125e-01 3.029520e-01 -7.510695e-01 6.865261e-01 3.063839e-01
## [6771] -1.253390e-01 -2.305192e-01 1.962576e-01 2.587398e-01 1.831354e-01
## [6776] -5.065914e-01 4.507290e-01 1.743539e-01 2.718222e-02 1.061924e+00
## [6781] 9.238981e-02 5.796868e-02 3.310130e-01 -1.642011e-01 -1.805534e-01
## [6786] 4.173178e-01 -7.782037e-04 -4.791751e-02 -3.263115e-01 9.141471e-01
## [6791] -4.195807e-01 8.007523e-02 4.775788e-01 -9.961534e-01 1.315083e+00
## [6796] -8.207184e-01 -4.088814e-01 1.952472e-01 -2.470744e-01 -1.588170e+00
## [6801] -1.886245e+00 4.014692e-01 -6.374136e-01 8.944380e-01 9.024006e-01
## [6806] 7.208458e-01 -1.262814e+00 -1.402204e+00 -2.894232e-01 2.634275e-01
## [6811] -3.927908e-02 3.938079e-01 2.604110e-01 -1.248193e-01 -3.739579e-01
## [6816] 1.383554e-01 1.039518e+00 3.412454e-01 6.409762e-01 3.495702e-01
## [6821] 6.615868e-01 7.045177e-01 2.075676e-01 9.527119e-01 3.818641e-01
## [6826] 8.392621e-01 8.410360e-02 1.479965e+00 8.248839e-01 8.768643e-01
## [6831] 1.625920e-02 2.887236e-01 2.567717e-01 -3.962782e-01 -3.117841e-01
## [6836] 6.552526e-01 2.787297e-01 9.380731e-01 4.622046e-02 3.116668e-01
## [6841] -6.304159e-01 -5.762249e-01 2.261266e-01 -1.287789e+00 -2.368810e-01
## [6846] -2.335836e-01 2.710685e-01 7.047040e-02 -7.402060e-01 4.260204e-01
## [6851] 1.206597e-01 -2.683268e+00 -1.881279e+00 -3.017902e-01 2.929858e-01
## [6856] -1.447663e-01 4.017908e-01 8.014264e-02 4.726516e-01 -3.859220e-01
## [6861] -1.267624e-02 -4.727690e-01 -4.404589e-02 1.652799e-01 1.865744e-01
## [6866] 3.561651e-01 -7.934930e-01 2.740442e-01 7.004755e-01 -5.379592e-01
## [6871] -1.411270e-01 3.229471e-01 3.668835e-01 1.918828e-01 -1.979043e+00
## [6876] 2.794136e-01 7.697079e-01 -2.134299e-02 4.058127e-01 1.222119e+00
## [6881] -3.131652e-02 -1.519293e+00 4.460488e-01 -8.234136e-01 -5.659229e-02
## [6886] 4.487030e-01 7.051812e-01 4.430730e-01 5.321712e-01 6.672372e-01
## [6891] 4.314101e-01 4.024747e-01 -7.904766e-01 -1.311758e+00 2.328230e-01
## [6896] -6.370920e-01 -5.622498e-01 8.628687e-01 -6.558066e-02 -1.304696e-01
## [6901] 5.544915e-01 6.473104e-01 2.477630e-01 2.420924e-01 3.648521e-01
## [6906] 8.671309e-01 -1.394376e-01 -6.214681e-01 7.479362e-02 7.803653e-01
## [6911] 7.673551e-01 1.438040e+00 -5.479327e-01 4.486420e-01 -3.296999e-01
## [6916] -5.708962e-01 -6.580852e-01 -5.549508e-01 -8.393669e-03 1.766009e-01
## [6921] 1.506412e-01 2.767391e-01 -3.121057e-01 -1.017677e+00 -2.643716e+00
## [6926] -2.094484e+00 -2.768124e+00 -2.935464e+00 -2.028974e+00 5.364006e-01
## [6931] -6.304600e-01 1.665467e-01 9.071551e-01 -3.113501e-01 -4.947001e-01
## [6936] 7.735072e-01 -1.043599e+00 -2.791265e-01 5.144933e-01 1.068676e+00
## [6941] 4.051296e-01 -9.213091e-01 9.414447e-01 -1.616681e-01 -3.999976e-02
## [6946] 4.170315e-01 -4.294760e-01 3.059584e-01 2.837721e-01 7.541639e-01
## [6951] 9.779558e-01 6.415687e-01 -3.267626e-01 1.791929e+00 -4.761020e-01
## [6956] 7.591047e-01 1.250782e+00 1.941402e-01 6.273993e-01 1.193296e+00
## [6961] 5.876249e-01 6.755015e-01 3.044822e-01 1.203690e+00 -1.807481e+00
## [6966] 8.455510e-01 -3.916441e-01 1.718720e+00 1.058592e+00 -1.532707e-02
## [6971] -7.054564e-01 -2.052179e-01 8.483483e-01 -1.729943e-01 2.407661e-01
## [6976] 8.773085e-01 6.038144e-01 -5.153325e-01 4.147005e-01 -4.522063e-01
## [6981] 7.419052e-01 2.405330e-01 -6.524451e-01 8.602183e-01 6.080560e-01
## [6986] 2.373792e-01 6.277100e-01 4.019757e-01 1.126162e+00 7.473123e-01
## [6991] 1.720727e-02 1.189055e+00 2.318944e-01 5.138717e-01 -6.588304e-01
## [6996] 3.604460e-01 -1.738490e-01 -6.540449e-01 -2.119140e-01 1.791161e-01
## [7001] 3.386481e-01 1.048190e-01 -5.438266e-01 7.419370e-01 -3.745999e-01
## [7006] 7.231058e-01 -1.162995e-01 -2.435184e+00 7.329476e-02 5.275289e-01
## [7011] -1.173730e-01 2.400668e-01 1.582966e-01 -2.039288e-01 1.050310e+00
## [7016] 1.717666e-01 4.493010e-01 3.022607e-01 7.037402e-02 7.107235e-01
## [7021] 6.798208e-01 1.170799e+00 4.684889e-01 8.061192e-01 9.757897e-02
## [7026] -1.400621e-02 -3.867809e-01 7.718296e-01 5.080302e-01 -6.236861e-01
## [7031] 1.187553e-01 4.110805e-01 3.448005e-01 1.549873e-01 7.201850e-01
## [7036] 7.689866e-01 6.138515e-01 5.474938e-01 3.203609e-01 -4.685511e-01
## [7041] 3.969112e-01 1.278687e+00 2.712026e-01 1.169223e-01 -9.102159e-01
## [7046] 5.119610e-01 -8.605914e-01 -4.438007e-01 1.861231e-01 -8.083016e-02
## [7051] 5.861027e-01 -1.695197e+00 -6.037990e-01 5.194342e-01 4.739736e-01
## [7056] 2.682818e-01 8.363227e-01 1.050498e+00 4.153998e-01 -5.480682e-01
## [7061] 1.282151e+00 1.001495e+00 1.793492e-01 4.623366e-01 5.868019e-01
## [7066] 5.849373e-01 6.310970e-01 8.480374e-01 -5.917733e-01 -4.529738e-02
## [7071] 2.709695e-01 7.127897e-01 1.474365e-01 2.329045e-01 4.864654e-01
## [7076] 3.267003e-01 1.164479e-02 4.503887e-01 6.140847e-01 2.682042e-01
## [7081] 3.408696e-01 9.819961e-01 1.001339e+00 1.099889e+00 1.405172e+00
## [7086] 4.705090e-01 -2.101407e+00 -8.009312e-03 1.284040e-01 5.480376e-01
## [7091] 2.779765e-01 5.749378e-02 3.054145e-01 6.556920e-01 3.458105e-01
## [7096] -4.769566e-01 3.283779e-01 3.111324e-01 -8.469448e-04 2.352813e-01
## [7101] 1.495403e-02 3.482651e-01 4.733521e-01 7.424809e-01 -2.200865e-01
## [7106] 4.807818e-02 1.111791e+00 7.270367e-01 -2.830573e-01 -1.308431e-01
## [7111] 9.620312e-01 9.908361e-01 -5.037731e-01 7.334538e-01 2.104850e-01
## [7116] -2.886657e-01 -4.027880e-02 1.480581e-01 5.033142e-02 -1.625841e+00
## [7121] 4.375085e-01 8.708453e-01 -3.903232e-01 7.722181e-01 6.711046e-01
## [7126] -3.061761e-01 -3.130277e-01 -4.597571e-01 8.146720e-02 -1.580892e+00
## [7131] 4.499226e-01 3.188387e-01 -8.829332e-01 4.608603e-01 5.902666e-01
## [7136] 5.225880e-01 1.055960e-01 6.167723e-01 2.657178e-01 -1.239208e+00
## [7141] -2.130795e-01 2.070981e-01 -6.877130e-01 6.144413e-01 1.931301e-01
## [7146] 1.343801e+00 7.915614e-01 -2.130018e-01 1.781061e-01 3.031931e-01
## [7151] -4.133642e-01 2.875475e-01 -2.548423e-01 -7.312627e-01 3.543715e-01
## [7156] -1.089759e+00 8.648577e-02 -1.164367e+00 1.197971e-01 2.752888e-01
## [7161] 6.395025e-01 3.458882e-01 -3.640506e-01 5.710787e-01 -2.263626e+00
## [7166] 9.482963e-01 7.459138e-01 1.209253e+00 1.006637e+00 1.321848e+00
## [7171] 7.839788e-01 2.250994e+00 9.389584e-01 1.762580e+00 1.363766e+00
## [7176] 1.602660e+00 1.537934e+00 1.033842e+00 9.061451e-01 1.188122e+00
## [7181] 1.285383e+00 1.162394e+00 1.108761e+00 -2.750802e-02 1.304648e+00
## [7186] 1.815281e+00 1.772586e+00 9.235459e-01 1.538788e+00 1.106197e+00
## [7191] 1.290790e+00 1.040771e+00 2.011200e+00 1.232838e+00 1.232061e+00
## [7196] 1.296942e+00 7.536518e-01 5.361675e-01 7.137997e-01 1.589959e-01
## [7201] 6.163838e-01 3.291548e-01 -6.156455e-02 1.011345e+00 1.168701e+00
## [7206] -1.193614e-01 2.204904e-01 1.019440e+00 4.961600e-01 8.400981e-01
## [7211] -1.618235e-01 9.996300e-01 -1.311951e+00 5.861804e-01 4.660343e-01
## [7216] 5.908883e-01 1.244708e+00 3.460436e-01 -6.022767e-01 8.935755e-01
## [7221] 3.873402e-01 3.230803e-01 -5.324226e-01 -7.660962e-01 -1.021646e+00
## [7226] -5.540968e-01 -1.660651e-01 -3.754546e-01 8.600312e-01 -1.641484e-02
## [7231] 1.147214e+00 1.144371e+00 8.886266e-02 1.206200e-01 4.580950e-01
## [7236] 3.536721e-01 4.695767e-01 1.160716e+00 -2.721655e-01 5.288497e-01
## [7241] -1.585919e-01 -3.667383e-01 -9.395645e-01 4.461471e-01 3.407919e-01
## [7246] -7.573022e-01 -1.322733e+00 -2.009668e+00 -2.981306e-01 -1.205648e+00
## [7251] 6.256537e-01 4.749843e-01 8.109901e-01 -3.456657e-01 1.938937e-01
## [7256] -4.528016e-01 -4.195430e-01 -1.976389e+00 -1.659998e-01 3.183022e-01
## [7261] 5.118688e-02 -1.193342e+00 -3.819611e-01 -2.871313e-01 -1.582533e+00
## [7266] -8.297477e-01 -3.759485e-01 -6.304768e-01 -4.231587e-02 -1.789425e+00
## [7271] -1.114136e+00 -2.321363e+00 -2.649708e+00 -6.164004e-02 1.030790e+00
## [7276] 1.042111e+00 8.854293e-01 -2.467066e+00 -2.286114e+00 3.745244e-01
## [7281] -2.272139e+00 -9.421514e-01 4.859429e-01 -1.308420e+00 -2.832329e+00
## [7286] -5.865201e-01 4.926393e-01 -1.672356e+00 -2.187303e+00 3.511986e-01
## [7291] -2.062211e+00 -2.212920e+00 -2.124445e+00 9.835972e-01 1.257920e-02
## [7296] -2.424156e+00 -2.126758e+00 -1.645733e+00 -7.761595e-01 4.293788e-01
## [7301] 6.988063e-01 1.852270e-01 -8.942541e-01 8.714745e-01 -5.299360e-02
## [7306] -1.508676e+00 5.454828e-01 4.477178e-01 1.356606e-01 -5.595753e-01
## [7311] -2.299170e-02 -1.434392e-01 1.106658e-01 -4.564613e-01 7.914048e-03
## [7316] -5.449569e-01 1.665867e-01 -5.815334e-01 2.058242e-02 -6.018630e-01
## [7321] -2.572107e-01 -1.031290e+00 -6.763632e-01 -5.971978e-01 -5.389444e-01
## [7326] -6.623880e-01 -2.232682e-01 -1.992991e-01 -6.590500e-01 -4.993515e-01
## [7331] -6.138068e-01 5.188800e-01 -2.352526e-01 5.551010e-02 1.685367e-01
## [7336] -3.670008e-01 5.058900e-01 -9.421718e-01 -9.075657e-01 2.876774e-01
## [7341] 1.423367e-01 -7.024582e-02 -1.373972e+00 -4.192214e-01 -1.282965e+00
## [7346] -9.857865e-01 -1.078866e+00 -8.220661e-01 -1.745529e+00 -7.116328e-01
## [7351] -6.520929e-01 -2.665410e-01 2.494524e-01 -9.002667e-01 -1.311778e+00
## [7356] -2.130417e+00 -1.499728e+00 -2.187966e+00 -2.490372e+00 -2.021311e+00
## [7361] -2.195286e+00 -2.254162e+00 -1.920493e+00 -3.069358e-01 7.943354e-01
## [7366] -4.576111e-01 1.055380e+00 -1.703969e-01 9.706843e-01 2.370325e-02
## [7371] 5.366324e-01 8.427609e-01 9.217133e-01 6.582919e-01 1.121335e+00
## [7376] 8.583833e-01 1.286667e+00 8.149333e-01 1.792886e+00 2.036169e-01
## [7381] 4.766445e-01 3.075252e-01 3.185671e-01 3.808732e-02 9.421138e-01
## [7386] 1.421931e-01 7.907205e-01 4.207416e-01 4.281939e-01 2.120097e-01
## [7391] 1.147008e+00 6.995379e-01 3.235426e-01 9.288928e-01 1.011446e-01
## [7396] -6.802490e-02 4.149228e-01 2.405803e-01 -2.685866e-01 1.518125e+00
## [7401] -3.345658e-01 2.108466e-01 -1.467947e+00 -3.120116e-01 1.656628e-01
## [7406] -4.969760e-01 2.028739e-01 -7.918399e-01 -3.031848e-02 -5.420845e-01
## [7411] 1.575428e-01 -1.974217e+00 -3.734104e-01 -1.278923e+00 5.759974e-01
## [7416] -9.883415e-01 1.897031e-01 -2.198076e+00 6.714627e-01 6.110044e-01
## [7421] 6.947349e-01 6.263542e-01 7.009738e-01 8.372148e-01 1.102370e+00
## [7426] -4.480049e-01 9.730859e-01 -1.915404e-01 9.224062e-01 -5.118917e-02
## [7431] 1.292449e-01 2.845616e-02 6.854015e-01 6.076623e-01 4.744906e-01
## [7436] 9.970510e-01 2.006917e+00 1.177980e+00 3.967766e-01 3.509250e-01
## [7441] 7.360309e-01 1.945062e-01 -1.865656e+00 1.448123e+00 6.575558e-01
## [7446] 1.471843e+00 6.101140e-01 1.001561e+00 1.852549e-01 1.399974e+00
## [7451] 5.863932e-01 7.329590e-01 9.757211e-01 6.003231e-01 1.126425e+00
## [7456] 1.030936e+00 6.630071e-01 8.696326e-01 1.478910e+00 1.178409e+00
## [7461] -3.611357e-01 7.090363e-01 1.141667e+00 5.918440e-01 -1.870299e+00
## [7466] 1.322755e+00 -3.883255e-02 6.647235e-01 5.223968e-01 4.355878e-01
## [7471] 8.289534e-01 4.662742e-01 7.173136e-01 4.911056e-01 7.183231e-01
## [7476] -3.056185e-01 -2.014477e-01 -3.163182e-01 2.552073e-01 -1.808563e-01
## [7481] 1.018419e+00 -1.031319e-01 5.710503e-01 4.579968e-01 3.585704e-01
## [7486] 6.994396e-03 6.702750e-01 8.110870e-01 -5.641276e-01 -4.633887e-01
## [7491] 8.912334e-01 4.703114e-01 6.563450e-01 7.590016e-01 6.447360e-01
## [7496] 1.191733e+00 8.990055e-01 -4.549555e-02 3.593775e-01 -2.030640e-01
## [7501] -2.041736e-01 1.682962e-01 7.302330e-01 2.928581e-01 4.576939e-01
## [7506] 1.572986e+00 7.138812e-01 1.436414e+00 1.592165e+00 8.986673e-02
## [7511] 1.268146e+00 6.374691e-01 2.457188e-01 1.336748e-01 3.925874e-01
## [7516] 7.469900e-01 1.308017e+00 1.517470e+00 2.357901e+00 1.283287e+00
## [7521] 8.693296e-01 8.634753e-01 1.463870e+00 1.908008e+00 -9.126758e-01
## [7526] 5.544557e-02 6.364596e-01 5.749864e-01 1.008325e+00 2.213551e-02
## [7531] -3.782360e-02 3.380791e-01 5.444017e-01 1.051124e+00 6.075904e-01
## [7536] 7.279118e-01 -2.168923e-01 -1.923632e-01 4.030849e-01 9.195979e-01
## [7541] -8.082400e-02 4.691002e-01 -3.031960e-01 -8.647657e-02 -6.158089e-01
## [7546] -1.117116e-01 -7.090780e-01 -1.797457e-01 -1.862060e-01 -2.405120e-01
## [7551] 1.194045e-02 1.292334e-01 1.263062e-01 -2.217372e-01 -2.651795e-02
## [7556] -4.821635e-01 -1.796107e+00 -1.289083e+00 -8.876420e-01 -8.304466e-02
## [7561] -4.953232e-02 4.678890e-01 -7.151347e-01 -9.606626e-02 -5.800763e-01
## [7566] -8.975341e-01 -1.605674e-01 -4.424944e-01 -1.090836e+00 -3.039025e-01
## [7571] -3.039020e-01 -2.383925e-01 -3.530607e-01 -3.737538e-01 -9.856555e-01
## [7576] 2.157389e-01 -1.632340e-02 -1.296048e+00 -3.556854e-01 -8.047697e-01
## [7581] -8.469629e-01 1.112658e-01 2.984100e-01 6.492795e-01 1.551745e-01
## [7586] -6.286287e-01 -4.677294e-01 5.903298e-01 6.871321e-01 3.366661e-01
## [7591] 1.613328e-01 -2.417232e-01 9.826860e-01 5.010984e-01 -2.868437e-01
## [7596] 5.400615e-01 -1.017188e-01 3.140553e-01 -7.840154e-02 -7.890232e-01
## [7601] -2.315144e+00 6.401945e-01 5.377397e-01 3.039614e-01 -2.377465e+00
## [7606] -9.246182e-01 -1.596020e-01 1.811045e-01 -3.635844e-01 3.236701e-01
## [7611] -3.847925e-01 -1.300979e-01 7.769177e-02 5.341474e-01 7.950260e-01
## [7616] 2.634964e-01 5.008361e-01 1.202315e-01 -4.229893e-02 5.765317e-01
## [7621] 3.525067e-01 -1.673859e-01 -4.661824e-02 -8.428479e-01 2.983299e-01
## [7626] -2.674894e-01 6.033684e-02 7.278136e-01 1.898208e-01 1.093714e-01
## [7631] 8.169793e-01 3.476435e-01 2.692602e-01 1.233272e+00 7.080818e-01
## [7636] 7.128673e-01 6.151406e-01 8.501352e-01 4.220183e-01 -2.578408e-01
## [7641] 1.801262e-01 5.099409e-01 7.721404e-01 2.974752e-01 -3.953358e-02
## [7646] -1.529668e+00 -1.091934e+00 -2.438804e+00 -8.587266e-01 -8.051714e-01
## [7651] -2.784272e-01 3.555828e-01 7.236038e-01 -2.001216e-01 1.383032e+00
## [7656] 6.925916e-01 -8.137323e-01 -1.146422e+00 7.719850e-01 1.360325e-01
## [7661] -3.181699e-01 -6.886453e-01 1.945286e-01 -1.184409e+00 -1.709366e+00
## [7666] -1.343397e+00 -1.246836e+00 2.074960e-02 6.040474e-01 -1.122060e+00
## [7671] -1.114742e+00 1.356440e-01 4.832798e-01 -2.132349e-01 -3.181239e-01
## [7676] -1.682089e-01 -2.815034e-01 -1.000181e-01 -6.919309e-02 -4.183051e-01
## [7681] 1.340441e-01 -1.602955e+00 -2.735262e-02 2.349705e-01 3.226918e-01
## [7686] 2.013025e-01 2.511600e-01 3.013343e-02 1.981946e-01 -2.018309e-01
## [7691] -2.686549e-01 8.676482e-02 2.743565e-01 1.127584e-01 -5.428482e-01
## [7696] 6.189079e-02 -6.161353e-01 2.372238e-01 -4.493632e-01 -9.827259e-01
## [7701] -5.341319e-01 -7.359706e-01 -8.409373e-01 5.072532e-01 -2.627039e-01
## [7706] -2.217181e-01 -1.226706e-01 4.596172e-01 -7.351936e-01 -2.863666e-01
## [7711] -3.217439e-01 -1.271453e-01 -2.634032e-01 -6.385548e-01 -1.028730e+00
## [7716] -9.524448e-01 -9.518232e-01 3.720054e-01 7.800256e-02 7.621552e-02
## [7721] -5.716754e-02 -7.171712e-01 2.577008e-01 -1.423847e+00 -1.858971e+00
## [7726] -1.289065e+00 -1.242672e+00 -2.288028e-01 -6.741652e-01 -3.361464e-01
## [7731] -2.926330e-02 -1.400650e+00 -2.172644e+00 -1.558222e+00 -4.338194e-01
## [7736] 7.970150e-01 7.633081e-03 2.970484e-01 -4.986880e-01 8.712004e-02
## [7741] -5.080183e-01 -8.031043e-01 2.969671e-01 -2.715277e-01 -1.690366e-01
## [7746] -7.198966e-01 -4.661132e-01 -2.276117e-01 2.860084e-01 -6.780728e-01
## [7751] -2.598649e-01 -2.615542e-01 -6.211668e-01 6.183749e-01 -1.750289e-01
## [7756] -9.610930e-01 -8.493326e-01 2.624017e-01 -5.826132e-02 -3.027551e-01
## [7761] -7.625263e-01 -2.426126e-01 -7.056743e-02 -2.102985e-01 4.729733e-01
## [7766] 8.096223e-01 1.471258e+00 -1.261667e-01 -1.015666e+00 -1.390219e+00
## [7771] -2.611716e-01 -8.376900e-01 -1.339045e+00 -1.937802e+00 -7.149912e-01
## [7776] -1.434189e-01 -1.860627e+00 -2.123440e+00 -8.726380e-01 -1.764149e+00
## [7781] -9.355770e-01 -7.648183e-01 -1.670666e+00 -3.023617e+00 -3.626572e-01
## [7786] -1.482073e+00 -9.248382e-01 -2.758306e-01 -6.866380e-01 -9.234908e-01
## [7791] -3.952524e-01 -1.184011e+00 -7.408898e-01 5.195435e-01 2.311337e-01
## [7796] -2.814809e-01 1.293061e-01 5.475141e-01 -6.859947e-01 -1.700218e-01
## [7801] -1.424541e-01 -2.342471e-01 -3.011470e-01 1.745696e-01 7.680459e-02
## [7806] 3.621980e-01 -4.255352e-01 -7.033080e-01 -6.683803e-01 8.505220e-01
## [7811] -4.657916e-01 3.881979e-02 -5.133876e-01 8.255988e-03 -1.105489e+00
## [7816] 2.597068e-01 -7.449117e-01 -3.643466e-01 -2.761929e-01 9.280183e-01
## [7821] -5.106522e-01 -1.120108e+00 5.228816e-01 -1.074857e-01 -1.209951e+00
## [7826] -1.303413e+00 -9.115266e-01 -1.740437e-01 -1.402887e+00 -4.085843e-01
## [7831] -2.133755e+00 -3.829277e-01 -6.244631e-01 -1.104581e+00 -6.389433e-01
## [7836] -3.413203e-01 1.349447e-01 -7.953214e-01 -2.713696e+00 -3.064092e-01
## [7841] 1.110808e-01 -2.853565e-01 6.200815e-01 -4.728577e-02 -9.341115e-01
## [7846] 6.558852e-02 -2.042855e-01 -1.027642e+00 -7.582346e-01 2.020018e-01
## [7851] -2.187030e+00 -4.797997e-01 7.123234e-01 6.457846e-02 -5.576391e-01
## [7856] 4.153741e-02 -7.191595e-01 3.460814e-02 1.335002e-01 -3.406988e-01
## [7861] -7.931613e-03 -6.351678e-01 -3.248201e-01 3.724716e-01 -3.453289e-01
## [7866] 9.965999e-01 -1.398273e+00 -9.001266e-02 2.107640e-01 -7.858281e-01
## [7871] -1.776244e-01 -9.062073e-01 -2.557747e-01 1.455258e-01 -3.559559e-01
## [7876] -4.748588e-01 -3.207339e-01 -7.306412e-01 1.224071e-01 8.415485e-02
## [7881] -1.001836e+00 -2.252605e-01 -2.016482e+00 5.185794e-01 4.830467e-01
## [7886] -5.499329e-01 -3.329148e-01 -8.208170e-01 -1.679629e+00 3.916054e-02
## [7891] -7.422006e-01 4.640142e-01 5.416982e-01 2.889143e-01 5.882465e-01
## [7896] 5.927989e-01 4.671680e-01 7.547855e-01 2.161711e-01 8.427858e-01
## [7901] 1.014421e+00 1.080506e-01 4.252499e-01 1.639591e+00 -1.729167e-01
## [7906] -1.871401e-02 -3.019345e-01 -4.606118e-01 2.758327e-01 7.941713e-01
## [7911] 9.516056e-01 -6.498033e-01 1.440362e+00 1.238757e+00 -8.145174e-02
## [7916] 1.599817e+00 9.168038e-01 2.143381e-01 5.236757e-01 -8.195739e-01
## [7921] -4.545278e-02 1.498113e+00 1.472508e+00 6.160730e-01 1.491458e-01
## [7926] 1.138809e+00 1.105808e+00 -5.214848e-01 -9.404192e-01 7.571164e-01
## [7931] -2.894427e-01 -4.992207e-01 -6.549537e-02 9.275862e-01 9.607881e-01
## [7936] -2.700994e-01 1.779345e+00 1.093271e+00 9.486848e-01 4.020534e-01
## [7941] 3.240126e-01 8.979726e-01 7.841660e-01 1.308126e-01 6.126861e-01
## [7946] 8.269388e-01 -2.389636e-01 6.979986e-01 8.917426e-01 8.142916e-01
## [7951] 3.415371e-01 -1.485088e-01 7.520978e-01 3.370165e-01 -3.715238e-01
## [7956] 1.713322e-01 -2.115256e-01 -2.930627e-01 4.992362e-01 -1.329091e-01
## [7961] 3.660085e-01 4.075382e-01 2.838498e-01 5.276843e-01 5.173681e-01
## [7966] 5.439515e-01 3.649207e-01 -6.520523e-01 -4.475136e-01 -7.123097e-02
## [7971] -3.513363e-01 3.791692e-01 -6.757130e-02 3.622183e-01 4.241111e-01
## [7976] 5.830647e-01 -8.712703e-01 8.136443e-01 2.441237e-01 2.161734e-01
## [7981] 9.010735e-01 -1.168567e-01 4.959368e-01 1.453125e-01 -3.294393e-01
## [7986] 1.242990e-01 1.276777e-01 2.161531e-01 4.087682e-01 1.971911e-01
## [7991] 3.163116e-01 5.508115e-01 1.005897e+00 7.567586e-01 1.639220e+00
## [7996] 9.067442e-01 6.838664e-01 2.723955e-01 -6.796809e-01 -1.231706e-01
## [8001] 4.995965e-01 5.072374e-01 -6.328874e-02 -1.032617e+00 -6.474278e-01
## [8006] 7.915749e-02 3.599060e-01 -5.565996e-01 -5.263133e-02 2.553836e-01
## [8011] -3.530460e-01 2.447669e-01 7.723418e-01 -4.458445e-01 -7.282621e-01
## [8016] 9.615376e-01 5.264803e-01 1.323022e-01 3.855034e-01 -4.658119e-01
## [8021] -2.186030e-01 3.259026e-01 6.801865e-01 1.383351e-01 8.609425e-02
## [8026] -3.906278e-01 2.274537e-01 1.828944e-01 4.879332e-02 4.380049e-01
## [8031] -1.032032e-01 -8.324223e-01 -7.858520e-01 3.654751e-01 -1.294844e-01
## [8036] 4.746220e-01 -7.838207e-01 -7.478469e-01 4.018754e-02 1.732425e-01
## [8041] 8.335304e-01 3.122490e-01 6.178331e-02 4.496678e-01 3.056135e-01
## [8046] 8.016394e-01 3.129532e-01 6.441718e-02 -2.732574e+00 -2.318425e+00
## [8051] -4.999977e-01 -8.242400e-03 -2.758172e-01 -9.857360e-02 3.394711e-01
## [8056] 2.884481e-01 2.203350e-01 2.392898e-01 2.093195e-01 1.081283e-01
## [8061] 1.704775e-01 7.644861e-02 1.435057e-01 4.821143e-01 -4.763351e-01
## [8066] -8.550290e-01 6.150230e-02 4.201076e-01 -1.722247e+00 6.458879e-01
## [8071] 2.312728e-01 6.210139e-01 -2.163111e-01 -2.711872e-01 9.639419e-01
## [8076] 5.196673e-01 5.704571e-01 2.640402e-01 3.871071e-01 4.491456e-01
## [8081] 2.097857e-01 -9.468871e-02 -1.854861e-01 8.776969e-01 1.278609e+00
## [8086] -7.817419e-01 7.675940e-02 -2.833532e+00 -8.352970e-01 -8.928705e-04
## [8091] -4.719381e-01 1.304700e-01 -9.393314e-01 -1.031651e+00 -6.205006e-01
## [8096] -5.382181e-01 -4.075227e-01 -1.912816e-01 -3.633513e-01 -5.299680e-01
## [8101] -2.763709e+00 -9.566864e-01 -8.370842e-01 -1.911262e-01 -2.840674e-01
## [8106] 2.184703e-01 -4.765682e-01 3.011729e-01 1.928970e-01 4.847560e-01
## [8111] 3.278340e-01 2.899244e-01 3.879618e-01 3.807217e-01 4.917630e-01
## [8116] 3.213709e-01 3.331634e-01 7.488663e-01 3.554274e-01 3.688515e-01
## [8121] 6.343285e-01 -7.382320e-02 5.494822e-01 2.667279e-01 7.345874e-01
## [8126] 2.947875e-01 3.558159e-01 -4.404915e-01 3.773347e-01 5.351574e-01
## [8131] -6.176575e-01 1.472042e+00 6.234685e-01 -1.146266e+00 1.185823e+00
## [8136] 2.470738e-01 1.737091e-01 -3.451499e-02 -7.976205e-01 -3.067199e-01
## [8141] -2.796704e-01 -1.378500e-01 -1.292297e+00 6.990088e-01 -2.673105e-02
## [8146] -1.614999e-02 -8.030736e-01 1.548319e-01 -6.042651e-01 9.186111e-02
## [8151] -1.401505e+00 -5.319104e-01 1.504349e-01 -1.659097e-01 -6.260630e-01
## [8156] 1.981723e-02 -2.161557e-01 -6.106505e-01 -9.897328e-01 -2.794373e-01
## [8161] 1.201538e-01 1.707106e-01 -5.574060e-01 1.366492e-02 5.456290e-01
## [8166] -1.221386e+00 -8.356856e-01 -5.099254e-01 -1.704621e-01 1.771737e-01
## [8171] -2.160003e-01 -3.510927e-01 -4.913591e-01 1.210085e-01 4.652574e-01
## [8176] 6.636550e-02 1.519112e-01 -2.543071e+00 -1.988811e+00 -9.533771e-01
## [8181] -7.889360e-01 -1.854573e+00 -2.539218e+00 -1.159302e+00 -6.292486e-01
## [8186] -4.357378e-01 -5.334644e-01 -5.281350e-01 -7.363591e-01 -1.318057e+00
## [8191] -1.011096e+00 -8.483327e-01 -4.573025e-01 -4.794889e-01 -6.700791e-01
## [8196] -1.019735e+00 -6.738544e-01 -7.524709e-01 -6.361779e-01 -5.073155e-01
## [8201] -4.816645e-01 -6.780961e-01 -2.377522e-01 -2.249497e-01 -6.314700e-01
## [8206] -1.018647e+00 -6.396425e-01 -1.080297e+00 -7.246444e-01 -5.638691e-01
## [8211] -5.829793e-01 -1.052704e+00 -1.436261e+00 -1.236831e+00 -2.025199e+00
## [8216] -1.602100e+00 -1.134785e+00 -9.136804e-01 -1.896725e+00 -1.570576e+00
## [8221] -8.367734e-01 -2.389490e+00 -1.179946e-01 -2.620445e+00 -2.531512e+00
## [8226] -9.641595e-01 -4.134419e-01 -4.389279e-03 -3.017790e-01 3.145970e-01
## [8231] -3.829041e-02 4.726528e-01 3.404034e-01 5.078748e-01 1.145738e+00
## [8236] 5.934205e-01 8.637607e-01 6.944563e-01 1.889662e-01 9.548689e-01
## [8241] 3.830986e-01 1.423474e+00 5.687220e-02 4.220960e-01 3.552720e-01
## [8246] 8.750092e-01 -1.250016e-01 9.759675e-01 -1.357840e-01 3.693177e-01
## [8251] 1.359835e+00 1.011990e-01 -3.160261e-01 1.007492e+00 1.127715e+00
## [8256] -8.212832e-01 -2.344112e-01 6.820423e-01 1.569846e+00 9.549925e-01
## [8261] 8.857916e-01 1.143900e-01 1.275144e+00 1.315578e-01 8.247632e-01
## [8266] 1.355438e+00 1.180494e+00 1.253502e+00 1.217192e+00 1.882571e+00
## [8271] 1.400309e+00 -1.335846e+00 -1.209552e-02 -3.447073e-01 9.213244e-01
## [8276] 9.112413e-01 2.845584e-02 6.621009e-02 1.351818e+00 4.188645e-01
## [8281] 1.821698e+00 -1.055469e+00 7.478102e-01 4.462248e-01 1.296476e+00
## [8286] 1.255723e+00 6.862062e-01 1.623868e+00 1.374238e+00 7.560286e-01
## [8291] 5.647392e-01 7.362968e-01 9.186367e-01 1.119310e+00 7.799243e-01
## [8296] 8.938404e-01 -2.523572e+00 -2.488972e+00 -3.757654e-01 -2.128005e-01
## [8301] -1.316969e+00 -1.924474e+00 -8.554951e-01 -1.169845e-01 -3.857248e-01
## [8306] 4.486793e-01 4.916596e-02 -9.701964e-02 7.143002e-02 2.117741e-01
## [8311] -8.939109e-02 -2.169327e-01 -4.313407e-01 6.592026e-01 1.386627e-02
## [8316] 3.776138e-01 7.023640e-01 4.328784e-01 1.962062e-01 8.824107e-02
## [8321] -2.344430e-01 4.179321e-01 -8.631493e-02 -6.243077e-01 -4.802659e-01
## [8326] -5.848840e-02 -8.685308e-01 -1.155164e-02 -4.046797e-01 -1.239915e-01
## [8331] -3.074192e-01 -5.897073e-01 -1.417539e+00 -1.649892e+00 -1.749368e-01
## [8336] -1.554381e-01 -8.491638e-02 -7.585455e-01 -8.652674e-01 -1.694520e-01
## [8341] 2.038665e-01 -3.164146e-01 -2.715757e-01 -1.551273e-01 -6.296311e-02
## [8346] 1.391864e-01 -5.541226e-02 2.311951e-01 2.735795e-01 3.711508e-01
## [8351] 1.448725e-01 -1.962228e+00 -4.381146e-01 3.610676e-01 1.266169e-01
## [8356] -4.062134e-02 1.233854e-01 2.439200e-01 5.653608e-01 4.720312e-01
## [8361] -5.853103e-01 7.968016e-02 2.315059e-01 1.956623e-01 1.231523e-01
## [8366] -2.381029e-02 -6.820269e-01 -1.330186e-01 2.859935e-01 4.681004e-01
## [8371] 2.531802e-01 -2.589826e+00 -2.200614e+00 -1.039896e+00 -6.204423e-01
## [8376] 2.448278e-01 2.497536e-01 -7.598518e-01 5.249365e-02 -2.927612e-01
## [8381] 5.617364e-02 3.389128e-01 9.039713e-02 1.190167e+00 4.784882e-02
## [8386] 7.064677e-01 7.144912e-01 6.862193e-01 -5.759777e-02 1.179530e+00
## [8391] 5.185787e-01 -2.851610e-01 -7.345963e-01 1.221694e-02 1.531201e+00
## [8396] 1.702870e-01 1.360482e+00 9.473018e-01 1.720095e+00 4.795884e-01
## [8401] -4.115804e-01 2.956603e-01 8.491949e-01 1.107643e+00 3.665212e-01
## [8406] -4.029340e-01 4.679662e-01 1.522899e-01 9.513441e-01 -1.120791e+00
## [8411] -1.793788e+00 1.203380e-01 -5.176905e-01 -1.855641e+00 -2.026640e+00
## [8416] 5.012042e-02 -1.628761e+00 6.115972e-01 5.879905e-01 -2.169543e-01
## [8421] -6.813906e-01 -1.019668e+00 -1.790305e-01 -1.284993e-01 -8.446877e-01
## [8426] -2.971658e-01 -2.818432e-01 -7.764646e-02 6.878104e-02 9.702451e-01
## [8431] 8.688204e-01 -1.411473e-01 8.678148e-01 -4.684661e-01 9.163554e-01
## [8436] 6.053444e-01 5.498061e-01 8.611386e-01 -1.803982e-01 -1.038871e-01
## [8441] 3.612128e-01 -4.565020e-01 2.397598e-01 1.121145e-02 -1.042652e+00
## [8446] -2.601469e+00 -2.188972e+00 -1.198931e+00 -1.084154e+00 -2.093499e+00
## [8451] -1.605798e+00 -1.385615e+00 -1.172006e+00 -1.375922e+00 -1.032919e+00
## [8456] -6.880057e-01 -5.315844e-01 -8.469797e-01 -7.864343e-01 -1.226560e+00
## [8461] -6.976576e-01 -7.545027e-01 -8.370061e-01 -7.421966e-01 -7.877207e-01
## [8466] -1.063885e+00 -3.895614e-01 -6.250668e-01 -1.375279e+00 -9.952318e-02
## [8471] -8.985977e-01 -8.912376e-01 -6.697074e-01 -7.681359e-01 -1.512979e+00
## [8476] -9.441015e-01 -7.219076e-01 -6.693655e-01 -7.399046e-01 -8.004094e-01
## [8481] -1.362631e+00 -8.250216e-01 -5.858971e-01 -1.113472e+00 -3.726104e-01
## [8486] -1.004687e+00 -5.831820e-01 -6.081407e-03 1.742480e-01 -4.707377e-01
## [8491] -4.231620e-01 -3.157044e-01 -3.386883e-01 -3.589163e-01 -2.050246e+00
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## [8501] -1.923163e+00 -1.122118e+00 -3.932211e-01 -6.134243e-01 -6.533591e-01
## [8506] -5.140105e-01 -4.977232e-01 -8.539977e-01 -9.195095e-01 -1.013997e+00
## [8511] -1.633768e+00 -7.298905e-01 -2.089839e+00 -2.536942e+00 -2.559885e+00
## [8516] -6.560336e-01 -4.104733e-01 1.486299e+00 -6.647003e-01 8.994654e-01
## [8521] 8.618836e-01 1.259078e+00 6.153585e-01 3.655360e-01 1.652572e+00
## [8526] 1.166560e+00 8.648594e-01 4.114224e-01 8.754761e-01 7.706931e-01
## [8531] 6.579475e-01 8.775074e-01 6.489591e-01 1.297090e-01 2.763768e-01
## [8536] 1.296660e+00 1.201167e+00 8.558710e-01 1.697514e+00 7.577234e-01
## [8541] 5.548131e-01 1.299957e+00 1.162880e+00 9.200761e-01 1.027513e+00
## [8546] 3.651941e-01 1.106699e+00 7.517109e-01 1.677909e+00 -3.022954e+00
## [8551] -1.554237e-01 -7.155259e-02 -6.916858e-01 5.347880e-02 -9.753254e-02
## [8556] -1.331705e+00 -2.811377e+00 -2.618440e+00 -2.784441e-01 -9.873743e-01
## [8561] -2.878899e+00 -1.176993e+00 -9.145024e-01 -7.588260e-01 -8.642725e-01
## [8566] -5.496018e-01 -3.330176e-01 -7.481889e-01 -7.115921e-01 -4.431294e-01
## [8571] -5.342589e-01 -6.357445e-01 -2.478600e-01 -2.621771e-01 -6.543849e-01
## [8576] -8.676513e-01 -5.292925e-01 -5.552521e-01 -5.785372e-01 -5.832227e-01
## [8581] -5.113361e-01 -5.642201e-01 -5.748572e-01 -5.023883e-01 -6.470451e-01
## [8586] -1.070883e+00 -2.395758e-01 -1.022302e+00 -7.109489e-01 -7.395221e-01
## [8591] -1.134404e+00 -5.422824e-01 -5.333144e-01 -5.961720e-01 -1.088942e-01
## [8596] -1.358186e-01 -7.302324e-01 -6.197788e-01 -7.841017e-01 -5.918488e-01
## [8601] -5.881891e-01 -1.247786e-01 -1.163360e+00 -8.233526e-01 -1.448452e+00
## [8606] -7.681563e-01 -6.680384e-01 -6.068294e-01 -7.328866e-01 -6.154759e-01
## [8611] -6.067888e-01 -6.038129e-01 -7.069473e-01 -6.550280e-01 -8.559477e-01
## [8616] -3.559811e-01 -1.321183e-01 -7.321824e-01 -4.411591e-01 -3.579921e-01
## [8621] -7.571366e-01 -8.034259e-01 -7.927685e-01 -2.756081e+00 -2.627762e+00
## [8626] -1.475957e+00 -9.992262e-01 -1.328296e+00 -1.167630e+00 -1.065817e+00
## [8631] -1.649115e+00 -1.085083e+00 -1.010863e+00 -1.054258e+00 -8.867863e-01
## [8636] -8.216717e-01 -1.277817e+00 -9.139135e-01 -5.734401e-01 -7.952437e-01
## [8641] -7.862166e-01 -8.157984e-01 -1.254232e+00 -8.141208e-01 -1.068971e+00
## [8646] -1.343941e+00 -8.597367e-01 -6.227537e-01 -1.062508e+00 -9.041412e-01
## [8651] -1.703369e+00 -9.886768e-01 -8.865532e-01 -1.120460e+00 -1.989977e+00
## [8656] -8.242039e-01 -1.734272e+00 -7.097757e-01 2.175697e-01 -3.090968e-01
## [8661] -1.820362e+00 -7.244889e-01 -6.550550e-01 7.247783e-03 3.328728e-02
## [8666] -6.875116e-01 -9.885214e-01 -1.035070e+00 -9.231184e-02 -4.733366e-01
## [8671] -6.090506e-01 -1.359198e+00 -1.292607e+00 -5.852326e-01 -7.388136e-01
## [8676] -5.228834e-01 -9.977499e-01 -3.296833e-01 -1.154283e+00 -9.248514e-01
## [8681] -1.973632e+00 -1.134474e+00 -1.164406e-01 -2.097703e-01 -1.270421e+00
## [8686] -8.959687e-01 -5.767494e-01 -6.243854e-01 -3.992726e-01 -6.074190e-01
## [8691] -2.527270e+00 -5.118679e-01 -9.334123e-01 -2.984192e+00 -8.025615e-01
## [8696] -9.930739e-01 -1.023821e+00 -1.386792e+00 -7.901474e-01 -1.181566e+00
## [8701] -7.678056e-01 -7.138619e-01 -5.466236e-01 -9.574633e-01 -1.014561e+00
## [8706] -7.307966e-01 -9.288598e-01 -6.001790e-01 -1.099485e+00 -6.420971e-01
## [8711] -6.702344e-01 -3.833939e-01 -1.441512e+00 -4.561371e-01 -1.177091e+00
## [8716] -9.001327e-01 -1.754315e+00 -1.564657e+00 -1.204219e+00 -1.301635e+00
## [8721] -1.201298e+00 -1.707611e+00 -7.411905e-01 -1.088392e+00 -8.164708e-03
## [8726] -4.349608e-01 -1.534298e+00 -2.218865e+00 -2.132465e+00 -2.875761e+00
## [8731] -2.976486e+00 -2.457093e+00 -2.389248e+00 1.086955e-01 -8.788063e-02
## [8736] -9.933665e-01 -8.872360e-01 -8.353574e-01 -9.810604e-01 -6.836825e-01
## [8741] -8.130574e-01 -9.168553e-01 -5.445946e-01 -1.041948e+00 -1.143413e+00
## [8746] -1.179989e+00 -3.989120e-01 -5.802265e-01 -6.896747e-01 -3.616721e-01
## [8751] -5.908636e-01 -1.029642e+00 -7.502201e-01 -5.356266e-01 -1.328709e+00
## [8756] -1.067907e+00 -1.366612e+00 -1.212243e+00 -1.879247e+00 -5.672365e-01
## [8761] -1.841665e+00 -1.321048e+00 -8.985570e-01 -6.553903e-01 -7.881236e-01
## [8766] -1.230541e+00 -8.630064e-01 -1.215259e+00 -1.194499e-01 -9.607307e-01
## [8771] -1.195634e+00 -5.971369e-01 -1.601133e+00 -1.385253e+00 -2.493602e+00
## [8776] -1.982426e+00 -4.069012e-01 -3.594205e-01 -1.406368e+00 -4.804213e-01
## [8781] -3.491043e-01 8.547571e-02 -6.511242e-01 -3.773193e-01 -5.096464e-01
## [8786] -4.934569e-01 -3.358673e-01 -1.912040e-01 -4.620880e-01 -8.131108e-01
## [8791] -4.489747e-01 -3.565775e-01 -4.537602e-01 -5.533976e-01 -2.560078e-01
## [8796] -8.287563e-01 -5.662777e-01 -2.980813e-01 -1.083762e+00 -1.541383e+00
## [8801] -2.153010e-01 -5.895519e-01 -2.818142e-01 -1.166931e+00 -1.960354e-01
## [8806] -5.081701e-01 -3.004029e-02 -5.657339e-01 -1.258784e+00 -5.513774e-01
## [8811] -8.617569e-01 -4.439102e-01 6.983013e-02 -3.614663e-02 -2.611040e-01
## [8816] -6.973616e-01 -3.547445e-01 -4.210245e-01 -9.740812e-02 1.848023e-01
## [8821] 3.316970e-03 -3.340803e-01 1.459602e-01 -7.005155e-01 -9.329461e-01
## [8826] 2.324383e-01 -7.043626e-02 1.932855e-01 4.184820e-02 1.494249e-01
## [8831] 3.216817e-01 4.728858e-01 5.302942e-01 -3.867890e-02 2.443084e-01
## [8836] -1.131855e-02 -8.588043e-01 9.858904e-02 4.870917e-03 -1.258745e+00
## [8841] -9.122609e-01 -1.147385e+00 -9.184498e-01 -1.288008e+00 -4.261436e-01
## [8846] -1.042708e+00 -1.195117e+00 -6.790435e-01 -6.655999e-01 -6.073207e-01
## [8851] -1.092670e+00 -1.308161e+00 -3.882647e-01 -2.798011e-01 -4.868745e-01
## [8856] -6.089792e-01 -1.321580e+00 -6.533698e-01 -3.424132e-01 -1.256814e+00
## [8861] -1.123667e+00 -9.638561e-01 -1.115027e+00 -9.118157e-01 -8.973565e-01
## [8866] -1.109283e+00 -9.338495e-01 -3.287220e-01 -6.522066e-01 -7.143484e-01
## [8871] -1.359261e+00 -9.156532e-01 -9.849995e-01 -1.099677e+00 -1.213389e+00
## [8876] -1.113120e+00 -1.068210e+00 -1.783707e+00 -1.353517e+00 -8.395725e-01
## [8881] -9.789832e-01 -1.114061e+00 -1.165879e+00 -1.219132e+00 -1.172613e+00
## [8886] -3.232762e-01 -1.072815e+00 -1.673807e+00 -9.398910e-01 -2.606391e-01
## [8891] -8.933465e-01 -8.705446e-01 -1.519027e-01 -4.329029e-01 -6.872137e-01
## [8896] -1.018571e+00 -8.736390e-01 -8.530411e-01 -1.214107e+00 -1.113343e+00
## [8901] -1.063184e+00 -5.629551e-01 -7.676271e-01 -2.428645e+00 -4.271342e-01
## [8906] -2.690119e-02 -1.150529e+00 7.695689e-02 -6.934777e-01 -7.126648e-01
## [8911] -1.410386e+00 -1.935103e+00 -7.433985e-01 -5.429308e-01 -4.497623e-01
## [8916] -6.189389e-01 -7.169518e-01 -7.420861e-01 -3.732495e-01 -3.393331e-01
## [8921] -3.842947e-02 -1.102545e+00 1.829331e-01 -7.182642e-01 9.440820e-02
## [8926] -1.218065e-01 -5.155170e-02 -5.667523e-01 -2.319323e-01 -2.051831e-01
## [8931] -3.937398e-01 -2.144698e-01 -9.835359e-01 -2.489916e-01 -4.253349e-01
## [8936] -7.332029e-01 -9.398285e-01 -8.010353e-01 -1.145142e+00 -6.655729e-01
## [8941] -1.048642e+00 -7.463253e-01 -8.172866e-01 -1.140902e+00 -1.144334e+00
## [8946] -9.454811e-01 -4.508723e-01 -6.668853e-01 -7.747908e-01 -7.199801e-01
## [8951] -1.288982e+00 -1.352474e+00 -6.036965e-01 -5.570620e-01 -7.574291e-01
## [8956] -3.444815e-01 -2.836140e-01 -5.281933e-01 -2.224447e-01 -1.814627e-01
## [8961] -5.515106e-01 -7.512718e-01 -1.170983e+00 -1.120210e+00 -6.510376e-01
## [8966] -3.876836e-01 -2.806869e-01 -5.061879e-01 -7.086750e-01 -6.016783e-01
## [8971] 1.199462e-01 -5.669955e-02 4.167357e-03 -3.120792e-01 -7.339100e-01
## [8976] -1.901585e+00 -1.770540e+00 -1.488593e+00 -1.710974e+00 -1.250329e+00
## [8981] -1.667292e+00 -1.294011e+00 -3.727213e-01 -6.109859e-01 -2.257916e-01
## [8986] -1.543122e-01 -2.337337e-01 -1.741676e-01 -2.326674e-02 -3.925767e-01
## [8991] 9.983658e-02 2.835724e-02 -1.699060e+00 -9.683826e-01 -3.290395e-01
## [8996] -5.831883e-01 -5.350407e-01 1.594888e+00 -1.054714e+00 5.445622e-01
## [9001] -7.253437e-01 1.712758e-01 -7.033857e-01 -1.259656e+00 -2.481344e+00
## [9006] -2.558251e+00 -4.677741e-01 -4.920584e-01 -2.992054e+00 -1.297393e+00
## [9011] -8.295333e-01 -2.056412e+00 -8.731608e-01 -1.089013e+00 -8.836325e-01
## [9016] -8.253694e-01 -9.492910e-01 -1.347872e+00 -1.048851e+00 -7.711149e-01
## [9021] -9.646257e-01 -1.417150e+00 -1.049394e+00 -8.915718e-01 -1.541305e+00
## [9026] -4.421231e-01 -5.549515e-01 -1.022189e+00 -1.201453e+00 -7.910021e-01
## [9031] -1.707766e+00 -1.235898e+00 -1.151052e+00 -8.013960e-01 -2.737194e-01
## [9036] -6.468048e-01 -9.103712e-01 -6.063312e-01 -4.243020e-01 -1.293617e+00
## [9041] -5.168087e-01 -1.315104e+00 -1.603732e+00 -1.048928e+00 -3.526466e-01
## [9046] -7.516162e-01 -7.521600e-01 -7.012925e-01 -1.220020e+00 -1.184798e+00
## [9051] -1.029352e+00 -5.881534e-01 -1.746065e+00 -1.201376e+00 -8.245147e-01
## [9056] -4.171396e-01 -6.471156e-01 -4.456655e-01 -5.188748e-01 -7.747349e-01
## [9061] -7.912352e-01 -2.788934e-01 -1.056168e+00 -2.474468e-01 -2.643374e+00
## [9066] -2.355648e+00 -5.699314e-01 -4.880916e-01 -9.348321e-01 -6.347594e-01
## [9071] -9.504762e-01 -3.293580e-01 -1.061790e-01 -8.323410e-01 5.375406e-01
## [9076] -8.586629e-01 2.913168e-01 5.727695e-01 -1.494315e-01 5.873676e-01
## [9081] -3.723092e-01 5.687883e-01 5.119026e-01 7.893537e-01 -5.818956e-01
## [9086] -2.608907e-01 8.771452e-01 -3.413424e-01 6.908563e-03 -6.480709e-01
## [9091] -7.189521e-01 -2.602068e-01 -6.786551e-01 -1.441495e+00 2.719756e-02
## [9096] 2.613759e-01 1.957828e-01 -7.189928e-01 -8.011079e-03 -7.022549e-02
## [9101] -1.102192e+00 -9.660594e-01 1.060889e-02 -6.600554e-01 -1.031974e+00
## [9106] -6.331310e-01 8.597924e-03 -1.137099e+00 2.367840e-01 2.241359e-01
## [9111] 6.618781e-02 -2.253177e+00 3.698389e-01 1.948789e-01 2.856665e-01
## [9116] -7.432631e-01 -2.934858e-01 -7.009754e-01 2.514227e-01 -3.620140e-01
## [9121] -1.487679e-01 5.114623e-02 3.010094e-01 1.056994e-01 3.741418e-01
## [9126] -2.794923e+00 -2.702059e+00 -3.687584e-01 -1.450442e-01 3.367577e-02
## [9131] -5.710137e-03 3.322310e-01 2.639625e-01 1.759623e-01 1.502336e-01
## [9136] 1.468149e-01 1.675568e-01 -4.498294e-01 -1.194013e-02 3.467429e-01
## [9141] -5.689654e-01 3.050261e-01 -6.448530e-02 -2.976929e-01 -1.858738e+00
## [9146] 2.613209e-01 2.001370e-01 1.653812e-01 5.527137e-01 -2.805190e-02
## [9151] -9.216975e-01 -8.939487e-01 -8.434695e-01 1.039405e+00 4.573180e-01
## [9156] 3.560808e-01 -7.370583e-01 3.390886e-02 -3.796185e-01 1.234360e+00
## [9161] 1.204856e+00 1.493794e+00 5.506476e-01 -3.081726e-02 7.557178e-01
## [9166] -1.335147e+00 -2.678779e-01 1.674014e-01 -3.720676e-01 2.133281e-01
## [9171] 1.389961e+00 7.662671e-01 3.684631e-01 1.136587e+00 6.819848e-02
## [9176] 2.292844e-01 2.500722e-01 4.647135e-01 7.284352e-01 9.914576e-01
## [9181] 1.121641e+00 5.106401e-01 9.224899e-01 8.079063e-01 -1.872822e+00
## [9186] -6.197460e-01 -6.376125e-01 3.071476e-02 2.029196e-01 1.163125e-01
## [9191] 3.328301e-01 1.943394e-01 2.820566e-01 -3.103633e-01 3.364193e-02
## [9196] 1.640572e-01 1.424563e-01 -2.786680e-01 5.140781e-02 -6.936000e-03
## [9201] -4.285645e-01 4.234746e-01 -1.715701e-01 4.134813e-01 1.910618e-02
## [9206] -1.846923e-01 -1.882248e-01 -2.788697e-01 -8.072335e-02 -7.740839e-01
## [9211] -7.818559e-01 -3.327717e-01 -4.624805e-01 3.458517e-01 -6.326041e-02
## [9216] -3.876831e-01 3.656360e-01 -1.817651e-01 -3.291380e-01 -3.662846e-01
## [9221] 2.713574e-01 -2.328406e-01 2.565192e-01 4.874365e-03 -1.470416e-01
## [9226] 1.417498e-01 2.109947e-01 -3.650728e-01 4.815154e-01 5.633783e-01
## [9231] -5.946121e-01 4.620341e-01 2.555097e-01 8.451658e-02 -4.210104e-03
## [9236] -4.808517e-01 2.367349e-01 1.854572e-01 5.625712e-01 1.422540e-01
## [9241] 7.426488e-01 1.628459e-01 2.719627e-01 1.285264e-01 3.979973e-02
## [9246] -2.019590e+00 -1.708896e+00 -1.086193e+00 -4.271515e-01 -4.378513e-01
## [9251] -1.539617e+00 -2.002375e-01 9.673001e-02 2.677814e-02 -8.146623e-01
## [9256] -4.994252e-01 -4.952863e-01 -1.521246e+00 -6.680965e-01 -2.723084e-01
## [9261] -8.006318e-01 -4.186137e-02 -7.460228e-01 -3.731938e-02 -4.625821e-01
## [9266] -7.993194e-01 -7.813517e-01 -1.032290e+00 -1.483091e+00 -1.168156e+00
## [9271] -1.513776e+00 -6.910098e-01 -9.484083e-01 -2.809893e-01 -6.506336e-01
## [9276] -9.112622e-01 -4.962957e-01 -5.856283e-01 -4.200854e-01 -1.036833e+00
## [9281] -1.754055e-01 -7.617693e-01 -3.824347e-01 -6.705189e-01 -7.203836e-01
## [9286] -6.906062e-01 -4.588467e-01 -5.114369e-01 -1.208835e+00 -4.107991e-01
## [9291] 4.215568e-01 -4.217007e-01 -3.873807e-01 -4.822647e-01 -8.785575e-01
## [9296] -4.116063e-01 -6.345841e-01 -5.255685e-01 -3.558873e-01 -8.647707e-02
## [9301] 2.385096e-02 -3.755708e-01 -4.696477e-01 -4.732814e-01 -4.360342e-01
## [9306] -4.328040e-01 -2.682708e+00 -1.905959e+00 -1.912198e+00 -1.442991e+00
## [9311] -1.054791e+00 -7.383135e-01 -5.879110e-01 -4.096307e-01 -5.962787e-01
## [9316] -1.860194e-01 -4.151517e-01 -6.318061e-01 -5.550327e-01 -3.184982e-01
## [9321] -2.227602e-01 -5.476306e-01 -6.323266e-01 1.602841e-02 -1.053850e+00
## [9326] -3.284994e-01 -2.244373e+00 -1.449973e+00 -2.325950e+00 -1.258275e+00
## [9331] -1.489561e+00 -9.727442e-01 -1.340817e+00 -9.156030e-01 -7.291777e-01
## [9336] -5.564435e-01 -3.640269e-01 -3.951965e-01 -3.292424e-01 -6.783005e-01
## [9341] -3.316519e-02 -6.572573e-01 2.386741e-01 6.340540e-01 -3.354563e-01
## [9346] -3.544458e-01 3.618114e-02 -1.299991e+00 -1.639493e+00 4.721142e-01
## [9351] -4.316896e-01 4.556013e-01 2.442453e-01 -1.011007e-01 -5.531265e-01
## [9356] -6.598815e-01 -1.037287e+00 -4.373692e-02 -6.229430e-01 -3.180028e-01
## [9361] -5.250512e-01 -3.693253e-01 -1.089378e+00 -8.911927e-01 -3.784611e-01
## [9366] -1.016912e+00 -5.996709e-01 -1.245301e+00 -1.270480e+00 -7.320996e-01
## [9371] -4.748920e-01 -6.845895e-01 -2.675709e-01 -4.506541e-01 -5.617670e-01
## [9376] -4.991298e-01 4.027929e-01 -2.642650e+00 -5.228974e-01 2.355045e-01
## [9381] -8.974065e-01 -1.747709e+00 -9.463919e-02 6.575238e-01 1.257130e+00
## [9386] 6.950076e-01 -7.412103e-01 -1.061723e+00 9.754874e-01 -2.020870e-01
## [9391] 1.239716e-01 -2.327355e+00 -2.219255e+00 -9.085509e-01 -3.340638e-01
## [9396] -8.197336e-01 -9.567495e-01 -6.889706e-01 -6.993064e-01 -1.195996e+00
## [9401] -7.674927e-01 -1.113794e+00 -1.263478e+00 -1.065554e+00 -7.941158e-01
## [9406] -1.054897e+00 -1.065876e+00 -1.083853e+00 -1.094470e+00 -1.039937e+00
## [9411] -1.231225e+00 -1.262171e+00 -1.287085e+00 -1.322696e+00 -1.024634e+00
## [9416] -9.261856e-01 -1.248176e+00 -1.369267e+00 -1.048262e+00 -9.720924e-01
## [9421] -9.634256e-01 -1.040621e+00 -4.305016e-01 -6.560538e-01 -7.717956e-01
## [9426] -2.029937e+00 -1.709274e+00 -1.316704e+00 -9.022167e-01 -1.193301e+00
## [9431] -1.553577e+00 -1.235870e+00 -9.903908e-01 -7.445496e-01 -1.597151e+00
## [9436] -9.547995e-01 -1.157066e+00 -1.435462e+00 -1.446783e+00 -8.786099e-01
## [9441] -1.086829e+00 -9.458111e-01 -1.240856e+00 -1.359293e+00 -1.469063e+00
## [9446] -9.255018e-01 -1.480384e+00 -8.969286e-01 -2.237491e+00 -1.981497e+00
## [9451] -1.002948e+00 -9.518484e-01 -8.719555e-01 -1.770486e+00 -1.115967e+00
## [9456] -8.087277e-02 -4.642952e-01 -5.857320e-01 -5.044032e-01 -7.798322e-01
## [9461] -1.415634e+00 -5.682034e-01 -6.498051e-01 -8.438551e-01 -8.748022e-01
## [9466] -1.143102e+00 -1.208141e+00 -1.432693e+00 -1.146469e+00 -9.051316e-01
## [9471] -9.770770e-01 -1.208611e+00 -1.496518e+00 -1.012084e+00 -1.230917e+00
## [9476] -1.286102e+00 -8.976542e-01 -1.070661e+00 -1.587453e+00 -1.126291e+00
## [9481] -1.436530e+00 -1.265479e+00 -1.071602e+00 -8.741344e-01 -4.989072e-01
## [9486] -1.396695e+00 -2.203350e+00 -1.481168e+00 -7.601497e-01 -1.865036e+00
## [9491] -6.061575e-01 -8.695790e-01 -1.068730e+00 -8.511098e-01 -9.593258e-01
## [9496] -8.242228e-01 -7.023909e-01 -3.652402e-01 -5.561959e-01 -2.301874e-01
## [9501] -3.853929e-01 -3.839570e-01 -1.238320e+00 -1.037535e+00
# See all distinct values in phonecall
unique(performance_labeled_p2$phonecall)
## [1] -1.132194042 0.312170953 1.097151995 1.411144376 -0.190216869
## [6] 1.678037882 -0.896699727 -1.917175055 -0.724003911 0.992487788
## [11] 0.469167143 1.165183663 0.913989723 0.390669048 0.793625951
## [16] -0.143118009 0.280771732 0.254605681 0.013878186 0.772693157
## [21] 0.092376284 0.333103776 0.123775527 0.432534695 -1.011830330
## [26] -0.535608470 -0.022754259 -0.300114214 -1.257791042 0.322637379
## [31] 0.212740034 0.275538504 0.160407975 -1.106027961 -1.069395542
## [36] 0.259838879 0.631396532 -0.331513435 1.133784413 -1.278723836
## [41] -1.833443761 -0.137884796 -0.294880986 -0.682138264 -0.184983656
## [46] -0.033220671 -0.886233330 -0.729237139 1.369278669 -0.054153498
## [51] -1.639815092 -0.755403161 0.908756495 1.421610713 -0.446643978
## [56] 1.159950376 0.034811012 -0.027987465 1.023887038 1.384978294
## [61] 0.961088538 0.929689348 1.808868051 0.647096157 1.086685538
## [66] 0.568598092 1.803634763 0.652329385 1.102385163 1.081452370
## [71] 1.222748876 1.641405463 0.872124076 1.123317957 0.531965613
## [76] 1.505342007 1.541974545 0.877357244 1.651871800 0.730827451
## [81] -3.053884506 -0.566262722 0.782955825 1.401347637 1.464592338
## [86] 1.520809770 1.057015896 -0.608425796 1.331075907 0.579167604
## [91] 0.501868606 -3.011721373 -0.538154006 0.213754252 0.445651203
## [96] 0.979716837 0.916472256 -1.859263778 1.429456353 1.071070194
## [101] 1.274858475 0.438624024 0.797010183 -1.936562777 1.246749759
## [106] NA -0.095441662 -1.550067902 -2.238731623 0.276998878
## [111] 0.719711185 -3.032802820 1.366211772 0.108346559 0.150509641
## [116] -0.137604743 0.319161952 -0.510045290 1.338103056 1.169450760
## [121] 1.204586625 1.492701054 1.295939922 -0.369501680 -2.041970491
## [126] 0.677548110 0.410515279 0.129428089 0.965662479 -1.304116607
## [131] -1.943589926 -1.318171024 -1.135464311 0.593221962 -0.341392964
## [136] -0.081387304 -0.123550378 -0.945730448 -0.545181155 -0.376528859
## [141] -2.477655649 0.607276320 0.775928676 -1.641421199 0.052129116
## [146] -0.741942227 -0.488963723 -0.615453005 -2.547927380 -1.451687336
## [151] -1.416551471 -1.374388456 -1.051138163 -0.643561721 -0.264093965
## [156] -0.285175532 -0.011115503 -1.880345345 -0.952757597 -0.699779153
## [161] 0.600249171 -0.221930906 -3.004694223 0.192672715 -0.917621732
## [166] -0.727887869 -0.243012443 -0.313284248 0.712684035 0.262944520
## [171] -0.053278584 -0.327338606 0.466732740 -0.320311427 0.368352205
## [176] -1.866717219 0.325589418 1.361843705 1.469662070 -0.602846444
## [181] -1.513312578 1.397783160 1.948854685 -1.351585031 0.307619691
## [186] 0.649044514 0.673004150 1.355853796 2.002763987 0.547216058
## [191] 0.044063706 1.847026229 0.247720599 0.259700418 1.116257429
## [196] 1.325904250 0.960519731 0.457367420 -0.812493265 -2.735254049
## [201] 0.397468328 0.085993066 0.385488510 -0.267411560 0.846711516
## [206] -0.243451923 0.337569237 0.403458238 0.091982976 -0.542947352
## [211] -0.357260197 0.505286694 -0.087714292 2.044693232 0.151882067
## [216] 1.283974886 0.864681244 0.565185785 0.427417874 -0.225482196
## [221] 0.145892158 0.157871976 1.104277611 0.625084877 0.056043524
## [226] 0.535236239 1.152196884 0.756862879 -0.075734474 -1.345595121
## [231] 0.571175694 0.265690327 0.900620699 0.990469277 1.062348247
## [236] 0.906610608 0.978489459 1.098287702 1.146206975 2.194441080
## [241] 1.128237247 1.427732706 1.763167500 0.762852788 -0.459088624
## [246] 0.014114161 1.224075794 2.242360353 2.086622715 1.936874866
## [251] 1.918905139 1.481641769 1.385803342 -2.299199104 -0.959498227
## [256] 2.583382607 2.033895969 2.023429394 3.148568869 2.091461182
## [261] 0.354036599 2.264157057 1.882132888 3.671889544 1.971097469
## [266] 1.149484038 2.258923769 1.259381294 1.290780544 1.039586663
## [271] 1.191349626 0.610463738 1.353579044 1.374511838 -0.164050832
## [276] 0.955855370 0.599997342 0.683728635 0.306937754 -0.472810030
## [281] 2.384520769 0.547665238 1.013420582 0.924456120 0.636629760
## [286] 0.751760304 0.448234320 0.139475137 -0.101252355 0.427301496
## [291] 0.416835099 0.788392723 0.982021391 1.510575294 0.374969423
## [296] 1.630939007 1.348345876 -0.765869558 1.615239382 2.007729769
## [301] 1.453009963 1.306480169 2.363587856 1.316946626 3.059604406
## [306] 1.719903469 2.531050444 1.426844001 1.767002344 1.238448501
## [311] 1.254148126 0.720361054 0.129008725 1.761769176 2.855509281
## [316] -0.666438639 1.887366176 2.536283731 -2.428465366 0.846200466
## [321] 0.558086097 -0.770050943 -2.287921906 0.551058888 -2.393329382
## [326] 0.487814277 0.073210657 0.930526614 0.164563999 1.042961478
## [331] -0.720860660 -0.060305763 0.944580972 0.417542487 1.324048638
## [336] -0.587344229 0.635385036 1.120260477 1.134314775 0.705656826
## [341] -0.397610396 0.515922964 0.909445047 0.480787098 -2.175486803
## [346] -0.460855007 0.761874318 -0.186794996 0.832146108 -1.184654593
## [351] 0.038074758 0.586194813 -1.001947880 0.459705561 -0.383556038
## [356] -0.404637575 -1.613312483 -0.807735264 2.018196344 1.568140507
## [361] 0.825025201 0.668029010 -0.519908905 -1.713079929 1.693737507
## [366] 1.301247001 0.746527076 0.819791973 1.018653870 -0.132651597
## [371] 0.474400371 0.579064488 0.662795782 -1.796811223 -1.336289048
## [376] 0.490099967 1.170416832 0.202273622 -0.996130645 -0.792035639
## [381] -0.090785943 0.296471328 -0.038453877 1.044819832 -0.488509625
## [386] 0.809325576 0.066210255 -1.482818842 -0.430944353 -0.258248568
## [391] 0.223206446 -0.216382906 -0.420477957 0.060977045 0.134241939
## [396] 0.186573997 -1.817744136 -1.488052130 0.238906071 0.170874387
## [401] 0.118542314 0.102842696 0.327870578 0.024344599 0.997721016
## [406] -0.007054640 0.458700746 0.176107585 0.270305306 -0.640272617
## [411] 1.746069551 0.898290098 0.286004931 0.804092348 1.000798464
## [416] 1.155396342 1.007825613 0.199699894 0.312134773 0.122400917
## [421] -0.931676090 -2.561981916 2.328935385 2.778674841 0.565113246
## [426] -1.922508478 -3.039829969 -1.027529955 1.180883288 1.275080919
## [431] 1.091918707 0.945388973 1.714670300 2.190891981 1.034353495
## [436] 1.405911088 1.929231763 1.479176044 0.401135474 0.526732445
## [441] 0.777926326 -0.075086325 0.516265988 0.249372482 0.584297717
## [446] 0.301704556 -0.085552737 0.542432070 -1.786344886 0.500566363
## [451] 0.181340799 -0.399545133 -0.912399352 -0.341979861 -0.734470367
## [456] -0.368145883 -2.293965816 -1.430486798 0.437767923 -0.572240949
## [461] -1.215925336 0.008644980 -0.938565373 -0.833901286 -0.211149693
## [466] 0.338337004 0.715127885 0.380202651 0.798859179 1.285547376
## [471] 0.903523266 0.814558804 1.060519457 0.443001121 0.840724826
## [476] -0.043687087 1.264614582 1.589073300 1.029120207 1.610006213
## [481] -2.022454739 1.769157410 2.434037447 1.631389499 0.696963787
## [486] 1.218085885 2.463986874 1.523571134 0.481327057 1.439712524
## [491] 1.547530770 2.290279627 0.840721607 1.421742797 2.230380535
## [496] 1.625399590 0.810772061 2.188451052 0.493306875 -0.369240016
## [501] 0.301629782 0.637064695 0.415438056 -1.495342851 0.008124253
## [506] 0.798792243 0.133912340 1.122247338 0.283660054 -0.141623467
## [511] 1.008439064 1.930884957 0.487316966 0.876661062 -0.429139107
## [516] 0.613105059 -0.411169380 0.720923424 0.229750887 0.097972885
## [521] -0.405179471 1.260015249 0.601125240 -0.974220812 -0.093704201
## [526] -2.208142042 0.774832606 0.750872970 -2.112303495 1.134227157
## [531] 1.870985866 0.355538964 -0.051774837 0.631074786 0.726913333
## [536] 1.894945502 3.757807255 2.535865784 1.793117046 2.026723623
## [541] 1.475651979 2.499926329 -2.166194201 -1.478120685 -1.899843097
## [546] 0.948633313 0.778464615 0.793261886 1.917855144 0.904241502
## [551] 0.401133955 1.111403465 1.562720418 1.296369433 0.371539384
## [556] 0.911640108 1.407348990 1.133599281 1.052214265 1.081808805
## [561] 1.214984417 0.874646902 0.223566592 1.555321813 0.186573386
## [566] 1.459139466 1.237180233 1.651504159 1.044815660 1.140998006
## [571] 1.821672916 -0.997209013 0.815457821 0.201370671 0.808059156
## [576] -0.020588532 1.703294635 -0.420115083 0.889444232 -0.190757260
## [581] 0.171776116 0.423329890 -0.804844379 0.356742114 0.304951638
## [586] 0.452924430 -0.013189891 -1.692681193 -0.064980373 0.667484999
## [591] 0.882045567 0.763667345 0.127384275 -1.759268999 -0.035385814
## [596] -0.198155895 0.090391070 -0.767851174 0.630491793 0.068195149
## [601] 0.867248297 0.290154368 1.015221119 0.238363877 0.771065950
## [606] 0.009006029 0.119985633 0.623093188 0.023803309 -0.146365419
## [611] -0.412716448 0.260559797 0.001607389 0.704478204 0.674883664
## [616] 1.348159909 -0.309135497 1.766761065 1.078097343 2.497587681
## [621] 2.090011358 1.935413361 0.839173257 2.132174492 -1.128437161
## [626] 2.300826788 2.364071369 1.345130205 0.508895814 0.874309182
## [631] 1.485673785 -1.992780209 -1.044110894 1.626217365 0.698629677
## [636] 0.452678382 1.534864068 2.279745102 2.610022545 2.799756527
## [641] 0.347270668 1.619190216 1.675407648 2.097038507 1.471619487
## [646] 0.811064541 1.787842512 -2.554954529 1.288912773 1.513782501
## [651] -0.278148353 1.239722490 -0.074360125 1.612163067 1.415402055
## [656] 0.241862968 0.972689688 2.764620543 0.529977381 -0.594371438
## [661] 0.621330678 1.352157354 0.396460921 1.373238921 -2.669301748
## [666] -0.982411742 -0.649472952 -0.760452569 -0.523696065 -0.508898795
## [671] -0.501500130 -1.344945073 -0.960215807 -1.330147862 -0.893628061
## [676] -0.938019931 -0.849236250 -0.827040315 -0.952817202 -1.048999548
## [681] -0.471905589 -0.331331402 -0.723459363 -0.101973571 -0.242547736
## [686] -0.050183091 -0.249946371 -0.161162689 -0.479304224 -0.294338226
## [691] -0.486702859 -0.553290606 -0.686466157 -0.516297400 -0.042784452
## [696] -1.108188629 -0.619878352 -0.886229396 -0.878830791 -0.923222601
## [701] -0.782648444 -1.300553322 -1.337546468 -0.819641650 -0.967614472
## [706] -1.174776435 -1.263560057 -1.640890718 -0.568087876 -0.930621266
## [711] -0.301736861 -0.072379015 -0.427513748 -0.864033520 -0.494101495
## [716] -0.205554530 -1.026803613 -0.708662033 -0.383121908 -0.368324608
## [721] -0.597682476 -0.353527337 -0.457108289 -1.529911160 -1.485519290
## [726] -1.581701636 -0.390520543 -0.346128702 -0.738256633 1.251977563
## [731] 1.577517748 1.510929942 1.399950385 0.615694523 -0.397919178
## [736] 1.873463392 -3.113220215 -3.098422766 0.060796510 0.475120366
## [741] -1.618694782 1.799476981 -2.787679911 1.466538191 2.576334238
## [746] -0.257345021 0.845052361 -0.442311019 1.688497305 0.926437378
## [751] 0.149580196 1.007822394 1.414747596 1.540524483 0.526910841
## [756] 1.000423789 0.319748908 0.445525795 1.037417054 -1.707478523
## [761] 0.956031978 -1.285755992 1.281572104 0.919038773 1.385153055
## [766] -0.989810407 1.473936796 -2.617511272 -1.145181775 3.367988586
## [771] 0.297553003 0.556505382 0.053397872 -0.124169491 0.719275475
## [776] 0.859849632 1.126200676 1.064043045 0.726738393 0.656466603
## [781] 1.014852762 0.248890162 0.993771255 -2.048997641 1.710543633
## [786] 0.298080415 -0.861404300 -0.601398587 0.255917341 -0.531126797
## [791] -0.200849354 0.206727073 -0.334365785 -1.156545877 0.354297847
## [796] -1.353306890 -1.100328445 -2.147378206 -0.439773470 -0.418691933
## [801] -0.362474501 0.537004530 -1.008975029 0.740792751 -1.887372494
## [806] 2.729484558 1.851087213 1.359184623 2.047848225 1.253776908
## [811] 1.436483622 1.640271783 2.082984209 2.307853937 -0.453827828
## [816] 0.684575319 0.888363540 1.837032795 0.937553763 1.724597931
## [821] -1.472768903 0.951608121 1.668380499 2.258663654 1.893250227
## [826] 0.902417898 0.431596845 2.392179966 1.499728203 0.522950172
## [831] -0.973839164 -0.847349942 1.021879911 2.040821075 -1.107355595
## [836] -2.969558239 -2.639280796 -0.067332938 -0.257066786 -0.657616079
## [841] -1.788992047 -0.622480154 -1.669529915 -0.446800649 -0.495990932
## [846] -2.449547052 -0.552208364 -1.381415606 -2.716579676 -0.671670437
## [851] 0.305107594 -0.432746291 -1.507904768 -0.172740638 0.080237836
## [856] 0.066183478 -0.144631922 -0.193822175 -2.154405355 -3.025775671
## [861] -1.283035040 0.157536820 0.670520961 0.614303529 -0.517072439
## [866] 0.291053236 0.382406563 0.572140455 -0.151659101 -0.158686280
## [871] -2.070079327 0.143482462 -0.390583217 -0.791132450 -0.875458658
## [876] 0.016993217 -2.611171961 -0.130577564 -0.889513016 -1.016002178
## [881] -0.228958085 -0.819241166 -0.748969376 -0.355447322 0.284026057
## [886] -0.046251401 -0.840322733 -2.330084801 -2.273867369 -1.760883331
## [891] -0.763023734 -2.920367956 -1.697638631 -0.481936544 -1.023029447
## [896] -2.033018589 0.645289063 0.267958432 0.364140749 -0.701263428
## [901] 0.586099982 0.312350273 -0.220351815 0.193972036 -0.323932767
## [906] -1.307951927 -0.560689270 -0.841837585 -0.464506924 -0.094574936
## [911] -0.175959975 -0.027987171 -2.513930321 -0.797445714 -0.538493335
## [916] 0.504714906 0.978227913 0.164377466 0.682282269 -1.093391299
## [921] -1.189573646 0.349343479 0.467721730 1.104004741 0.741471410
## [926] 0.282755703 -0.575486541 -0.375723243 0.430728525 0.593498588
## [931] 0.208769307 0.156978831 -0.612479746 -0.131568134 -1.012006283
## [936] 0.082992427 0.253161162 -0.405317813 0.460323066 1.049988627
## [941] 0.691602468 1.176477909 0.818091750 0.024020396 -0.994920671
## [946] -1.915481210 -2.217649937 0.059156295 1.099178910 1.956494927
## [951] 1.886223078 0.860254824 0.087265015 1.162423491 0.227808610
## [956] -0.559235513 -0.664643228 -0.755996585 0.881336331 0.171591178
## [961] -0.896540165 -0.292202711 -1.634394050 -1.544708371 -0.908425331
## [966] 1.444342256 1.533125877 1.607112288 1.274173498 2.258192539
## [971] 1.436943531 0.785863221 1.814274192 2.065828085 1.311166644
## [976] 1.525727272 1.518328667 1.732889175 0.563904047 1.481335402
## [981] 2.169409037 1.392551780 1.170592546 -0.434912384 0.933836043
## [986] 0.216167957 1.288970828 -1.241364121 1.377754450 1.244578958
## [991] 0.660086334 0.097789712 0.341944844 -1.411532879 0.045999229
## [996] 1.422146320 1.118802071 0.608295858 0.275357068 0.837653697
## [1001] 1.148396611 2.354374886 2.117618561 0.970829248 0.327147543
## [1006] 0.230965227 0.334546208 0.105188347 -0.582885206 -0.693864763
## [1011] -0.079777651 1.755085111 0.963430583 1.092151761 1.584054351
## [1016] 1.478646636 0.825118899 1.260804057 1.106206059 -1.838182330
## [1021] 1.541891217 0.853227615 1.759733796 -0.032197043 -0.179767817
## [1026] 1.085124612 0.185645536 0.094292194 -0.306257069 -1.599258184
## [1031] 0.494841456 0.600897253 0.689680934 -1.004607677 -1.737073064
## [1036] -2.358558893 -0.745655239 -0.116770856 -2.780281305 -0.627277017
## [1041] 0.075593792 -0.279540926 -1.204370975 0.852451026 -0.360925972
## [1046] 0.386336684 -0.087176293 -2.565720797 0.748870015 0.245762512
## [1051] 0.800660551 -2.106313467 1.337884068 -0.375229925 1.266005158
## [1056] 0.169851795 -1.291685939 -0.501017988 1.589460135 -1.261736393
## [1061] -2.723274231 0.529246330 1.032398701 0.475337148 -0.111673921
## [1066] 1.391793251 -1.141938210 0.050053615 -1.902656674 0.678994060
## [1071] 0.643054605 0.217771068 0.289649963 -1.501332760 1.110267520
## [1076] 0.463357329 -0.027815200 0.175841704 0.127922431 -0.668735445
## [1081] -0.237462014 -0.171573013 -0.836452901 0.074013248 -0.465078533
## [1086] 0.684983969 0.235740796 -0.159593195 -0.099694103 0.115942612
## [1091] 0.367518783 -1.327625394 1.499611497 -0.183552831 -2.148242950
## [1096] -1.752908945 1.026408792 0.439397693 1.050368428 0.002134344
## [1101] 1.751187682 1.559510589 -0.620816171 0.595135331 0.655034423
## [1106] 0.888640881 -1.195847392 -0.734624445 1.505601406 1.811086774
## [1111] 1.236055613 1.248914957 2.243224144 2.128093719 2.049595594
## [1116] 1.468709588 2.609548569 2.557216644 0.395902276 0.694195032
## [1121] -2.100337267 0.709894657 0.003411773 0.741293907 0.919222891
## [1126] 0.406368673 1.772235632 0.762226701 0.348803401 0.369736224
## [1131] -0.870533705 -0.273948163 0.589530885 1.280314207 -1.624115467
## [1136] 0.615696907 0.704661429 1.683271050 1.793168426 1.175650001
## [1141] 0.265072107 0.573831260 1.976330638 1.243681669 1.050053120
## [1146] 1.562907338 0.987254620 1.154717207 0.343570203 1.594306588
## [1151] 1.144250751 0.971554995 1.521041632 0.893056870 1.332646251
## [1156] 1.903065681 1.207049251 1.128551126 0.856424451 0.940155745
## [1161] 0.484866768 0.453467548 0.657562613 0.045277424 -0.064619914
## [1166] 0.029577807 -0.410011530 -0.692604721 -0.917632580 -0.362912685
## [1171] 0.019111393 0.411601871 0.422068298 0.678495407 0.463933945
## [1176] 0.511032820 0.050510634 -1.875309348 -0.289647788 -1.859609723
## [1181] 1.186116457 1.829800844 -1.524684548 0.641862988 1.311713457
## [1186] 1.740836382 1.447776794 1.212282538 1.515808463 -0.708304286
## [1191] 0.191807210 0.207506821 0.725594282 0.505799592 -0.457110405
## [1196] -0.127418384 1.217515707 -1.194992542 0.076676667 0.165641174
## [1201] -1.315356255 -0.540841699 -0.629806221 -1.503751755 -1.085095167
## [1206] -0.059386704 -0.598406971 -0.122185186 0.317404151 -0.116951980
## [1211] -1.781111598 -0.378612310 -0.012287846 -0.069853120 -0.797268808
## [1216] -0.080319531 0.108075902 -1.734012842 0.228439659 0.626163363
## [1221] 1.570119143 1.673700094 1.658902764 -2.402950764 2.598530054
## [1226] 1.592314959 0.408532590 1.895659208 -1.041600823 1.629308224
## [1231] 2.450557232 1.866064668 1.636706829 1.769882321 2.428361416
## [1236] 1.192788482 -1.071195483 0.489917636 1.022619724 1.584916353
## [1241] 1.163193941 0.415931225 1.858666062 1.888260603 1.207585692
## [1246] -1.078594089 1.784679651 1.333362579 -1.838676929 -0.394311935
## [1251] -0.268714964 -0.467576802 -0.593173742 -0.237315729 -0.676905096
## [1256] -0.546074927 -0.247782141 -0.718770742 0.620930135 -0.242548928
## [1261] 0.882590473 0.291238129 0.097609490 -0.839134455 -0.582707345
## [1266] -1.111261249 0.155174762 0.233672857 -0.205916494 -0.153584421
## [1271] -0.179750457 -0.373379081 -1.074628711 -0.943798602 -0.415244758
## [1276] -0.148351222 0.197040409 -0.321047038 -0.650739014 1.500108838
## [1281] 0.845957994 -2.220700979 0.149941549 1.070985913 1.343112588
## [1286] -0.153764054 -0.212953180 -0.057581734 0.378938049 -0.227750450
## [1291] 0.016404670 -1.367141008 0.142181545 1.059612870 2.028834820
## [1296] -1.811059475 -2.010822773 0.571302652 -0.730857968 0.822856426
## [1301] 0.179174751 -1.973829508 -0.531094670 -0.775249839 -2.306768417
## [1306] -2.765484095 -2.994841814 -3.083625555 -1.545617342 -2.189301729
## [1311] -0.776336014 -1.289190292 -2.288732767 -0.441410780 0.495333195
## [1316] -0.158817634 0.040044218 -1.775878429 -0.169284046 -0.315813810
## [1321] -1.079861999 -0.279181391 -0.949031830 -0.567007720 -0.786802411
## [1326] -2.011372805 -0.619339824 -0.603640199 -0.818201661 -0.608873367
## [1331] -1.064162374 -1.959040642 -0.744936764 -0.624572992 -0.310580611
## [1336] -0.671671867 -0.514675677 -0.928098977 -0.661205471 -0.284414589
## [1341] -0.404778332 -0.655972242 -0.635039449 0.055743840 -0.645505846
## [1346] -0.174517244 -1.017063498 -0.352446258 -1.681680679 -1.168826461
## [1351] -0.347213060 -0.964731455 -1.053695917 -1.655514717 -1.200225711
## [1356] -1.058929205 -0.739703536 -0.498976052 -0.881000102 -0.985664248
## [1361] -0.221616104 -0.703071117 -0.802502036 -1.660747886 -1.032763124
## [1366] -0.771102786 -0.687371492 -0.478043228 -1.006597042 -1.896242142
## [1371] -0.556541324 -1.163593292 -1.906708598 -0.305347413 -2.528727531
## [1376] 1.370355844 -0.183358610 -0.590283811 0.549106777 0.711876869
## [1381] -0.138966769 -2.025619984 -1.122985959 -0.656871557 -2.676700354
## [1386] -1.315350533 -0.671668887 -1.426330209 -1.056398153 -0.634675682
## [1391] 0.756268680 0.697079539 -0.286939561 -0.753053904 -2.723607063
## [1396] 0.220781431 2.139201641 2.202446222 -1.072219729 -1.704665899
## [1401] 0.234835789 1.183505058 1.127287626 2.989490271 2.251636505
## [1406] 1.450537920 2.230554819 1.682434916 2.216500521 3.108952284
## [1411] -0.207876533 1.302967191 0.958635330 1.408374786 0.269971699
## [1416] -0.088414483 -1.268980742 0.544031739 0.115373738 -1.404320836
## [1421] -0.017521054 0.087143078 0.537198842 0.479633570 -0.048920292
## [1426] -1.325822711 0.830258429 0.736060679 -0.854834080 -0.812968433
## [1431] -0.933332205 0.359269828 0.071443461 -0.096019149 0.521499217
## [1436] -0.389078707 -0.326280236 -0.493742853 0.081909873 -0.860067308
## [1441] -1.221158504 -1.137427211 -0.828668058 -2.136969805 -1.477585673
## [1446] -0.980431080 -1.268257380 -1.582249761 -1.210692167 -1.435719967
## [1451] -1.090328336 -1.283957005 -1.791578054 -1.346755505 -1.739246011
## [1456] -1.608415842 -1.723546386 -2.184068441 -1.540384173 -1.828210473
## [1461] -1.587483048 -1.702613592 -0.849600911 -1.671214342 -1.362455130
## [1466] -0.922865808 -1.247324586 -0.577474177 -1.645048261 -1.116494417
## [1471] -1.985206723 -1.189759254 -1.048462749 -1.236858130 -1.184526086
## [1476] -0.436177582 0.986744046 -0.025169862 1.703516364 -1.297089458
## [1481] 1.506755352 2.153255939 1.591081500 0.045101937 -0.959784806
## [1486] 1.148369193 -1.177627325 -0.109496020 1.322179794 1.845500469
## [1491] 2.065295219 1.939698219 2.463018894 1.897832513 2.012963057
## [1496] -0.990897477 0.113309108 0.385435849 0.558131635 -1.351988673
## [1501] -0.253015339 -1.843910098 1.432077169 1.002954245 1.583840132
## [1506] -0.111718766 1.400677919 1.557674170 1.698970675 2.756078482
## [1511] 2.667113781 2.625248194 -1.237776756 -0.123653740 -0.698684990
## [1516] 0.139902249 0.804782152 -0.608836353 1.829056501 -2.657385111
## [1521] 0.103962794 1.613419771 1.014428973 0.379498601 0.667014241
## [1526] 1.242045522 1.373823524 0.121932521 0.205791250 0.714933515
## [1531] 0.223760977 0.187821522 0.930570185 0.241730705 -0.590866625
## [1536] 0.553205967 0.181831613 -2.160222769 -1.123968482 0.277670145
## [1541] 1.487631679 0.421427965 0.619094968 0.032083888 0.499296784
## [1546] 0.068023339 -0.794523537 -0.764573991 0.738893151 -0.303351015
## [1551] -0.177562922 1.002449155 -2.220121861 -2.567536592 -0.315330833
## [1556] 0.409448147 -0.363250107 1.176156521 1.301944613 1.212095976
## [1561] -0.674725354 -0.848432720 0.343559146 -0.788533628 1.493621588
## [1566] 0.768842697 0.511276603 -1.171887755 -0.483048260 2.062663078
## [1571] 1.367833614 1.170166612 0.373508692 -0.249441832 0.589145422
## [1576] 0.744883060 -0.902341902 -0.039795019 0.954529822 -0.926301539
## [1581] 0.313609600 -0.321320742 0.253710508 0.295639873 -0.578886807
## [1586] -1.129958391 0.361528873 -1.094018936 -1.579201579 -0.351270288
## [1591] -0.393199652 -0.015835382 -0.806503356 -0.069744565 -1.429453850
## [1596] -0.081724383 -0.255431741 -0.219492286 -0.117663831 -0.135633558
## [1601] -1.004170299 1.068338156 -2.297990561 -0.860412538 -1.728949308
## [1606] -1.585191488 -0.105684012 0.199801341 1.164176702 0.391478419
## [1611] -1.297675848 0.523256421 0.822751880 1.206106067 1.074328065
## [1616] 1.289964795 0.271680236 0.607115149 0.193811432 0.331579328
## [1621] -1.052089572 0.038073797 -0.033805110 -0.381219834 -0.728634536
## [1626] 0.211781159 -0.003855565 -0.662745535 -0.213502377 -0.447108805
## [1631] -0.201522559 -0.165583104 -0.417159289 -1.273716211 -1.225796938
## [1636] -1.740929127 -0.339290470 -0.063754655 1.403773069 0.026093978
## [1641] 0.858691335 -2.941449642 0.804037392 0.333216310 0.473759919
## [1646] 1.190532207 -0.039224222 0.642412245 0.136455283 -0.777078092
## [1651] 1.035934329 -0.706806302 -0.165713459 -0.348420143 0.326189131
## [1656] -1.739801764 -1.437633038 0.895390689 -0.503018081 1.211613774
## [1661] 2.265690804 -1.430605888 0.403488100 1.141342044 -1.536013603
## [1666] 1.822978497 0.389433742 1.548918486 -0.467882186 -0.235985264
## [1671] -0.214903727 0.747819901 -0.833295584 0.424569666 0.361325026
## [1676] -1.372921586 2.101927519 1.923998594 1.814101219 0.835491598
## [1681] 1.139017582 -1.378154755 -0.504209280 -0.561774552 0.144708350
## [1686] -1.692147136 -1.409554005 1.751302719 -2.262566566 -1.174059749
## [1691] -1.754945636 -1.807277679 -0.969964623 -0.357679486 0.364503026
## [1696] 1.008187413 -0.865300536 -1.765411973 0.767459929 0.552898467
## [1701] 0.976788163 1.730369925 -1.142660499 0.244139269 0.699428260
## [1706] 1.547207713 1.065752745 1.107618332 -0.195450068 -0.001821433
## [1711] -0.226849318 -0.425711155 -0.760636389 0.887823701 -2.337111950
## [1716] -1.086274028 0.754847109 -2.006834507 -0.250039607 -0.713833511
## [1721] -1.030056596 -0.580317080 -1.065192461 -0.924648881 1.317021489
## [1726] 1.218641043 0.663493752 -0.938703239 -2.316030502 0.649439394
## [1731] -0.903567374 -1.732774615 -1.247899175 -1.226817608 -1.170600176
## [1736] -1.290062308 -1.479796052 -1.395469904 0.375379384 -0.102468841
## [1741] -0.980866313 0.178618357 -1.311143756 -0.868431449 0.340243489
## [1746] -0.299229890 -0.629507363 0.733765543 -1.444660187 -1.205736041
## [1751] -2.020889044 -2.321565628 0.497316271 1.340761304 1.325963974
## [1756] 1.599713683 1.177991152 1.089207530 1.503531337 -1.455924749
## [1761] 1.695896029 1.829071522 1.096606135 0.541708112 1.451740861
## [1766] 2.147212982 -0.235149100 0.534309506 0.519512177 1.185389757
## [1771] 0.985626519 1.903057933 0.941234708 1.222383022 0.830255091
## [1776] 1.555945635 1.984603643 1.844059944 2.068929672 1.443510771
## [1781] 1.605135918 1.281885624 1.633244634 1.752706647 -1.409524322
## [1786] 0.628357887 1.879195929 1.225668192 1.801896930 0.768901467
## [1791] -1.929437637 -2.062613249 -1.670485258 -0.264743656 -0.901026726
## [1796] 0.438127160 -0.005791251 -0.642074287 -1.085992694 1.155795217
## [1801] -1.559505701 1.355558515 -0.790047109 -0.679067492 -0.716060698
## [1806] -1.833255410 -0.812214017 1.267831206 1.394320488 1.028907180
## [1811] 1.232695341 1.113233328 0.009966037 -0.734915018 0.031047577
## [1816] 1.830005646 0.867281973 -1.149518609 1.738652349 -1.957644343
## [1821] -1.817100763 -1.514932036 -0.650588870 -0.116523199 -0.678697586
## [1826] -0.411664754 -1.500877619 -2.266840219 -0.784105301 0.101319373
## [1831] -1.964671493 -1.493850470 -1.711693048 -1.458714604 -2.667389631
## [1836] -0.425719112 -1.528986335 -2.259813070 -1.388442755 -0.685724795
## [1841] -1.093301177 -1.058165312 -0.004088323 -2.943051338 0.038600590
## [1846] -0.812243044 -1.322749138 -1.270958662 -1.522512436 -1.552107096
## [1851] -0.272142291 -0.915823996 0.112586990 -0.856634855 -1.248762846
## [1856] -1.389336944 -2.047816038 -0.605081081 -1.714877129 -1.100790024
## [1861] -1.515113831 -1.404134274 -1.781464934 0.031201949 -2.027916193
## [1866] -1.360334039 -0.474909365 0.923499405 -1.367361188 -0.018142683
## [1871] -1.950617194 -0.805186808 -0.524099648 -1.332225323 -0.271121174
## [1876] -1.676557183 -0.854377091 -0.573289871 -0.636534512 -0.966811955
## [1881] -2.084133625 -0.798159659 -2.604144812 -1.219790459 -1.325198174
## [1886] -0.692751944 -1.574302912 0.896842837 1.718091846 -0.664270222
## [1891] 1.318565369 0.726674139 -0.168561339 1.266774893 -1.022140026
## [1896] -0.524977624 -1.088029027 -2.303980589 -0.914321721 -1.117978573
## [1901] 0.972499549 -0.207512468 -0.758584082 -0.387209743 -1.267726302
## [1906] -2.315960407 -0.566906989 -0.782543719 -0.554927170 -0.153603286
## [1911] -1.477373123 -1.010160208 -1.255746484 -0.746604264 -0.423149198
## [1916] -1.507322669 -1.764888763 -0.884372175 -1.058079481 -0.584876716
## [1921] -0.992190540 -2.100323677 -0.932291448 -0.824473083 -0.441118896
## [1926] -0.770563900 -1.980525494 -2.627435684 -2.351160288 -0.945418537
## [1931] -0.338730067 0.637890458 0.578701317 -1.152580500 1.851267457
## [1936] -0.449709654 0.652687728 0.393735319 -2.358193636 -1.142491460
## [1941] -0.910594523 -1.718720198 -2.182514191 -1.564122319 -1.824127913
## [1946] 1.654326081 1.197559476 2.378125668 1.773788214 0.789983034
## [1951] 1.380266070 2.483533382 -1.402497172 1.527836919 -1.486823320
## [1956] -2.573119402 -1.226566911 -1.374539733 -1.774066210 -1.211769581
## [1961] -0.316534132 -1.352343798 -1.818458080 -2.432545185 2.302584410
## [1966] 0.993025184 0.482519001 0.512113571 -0.834438920 0.734072745
## [1971] 1.777281046 -2.046414375 1.080317974 2.104592323 0.708943605
## [1976] 1.535550952 0.912600517 -0.776553810 0.661024332 -0.560917079
## [1981] -0.818483174 -2.429768562 -0.638785899 1.230065703 -1.405494213
## [1986] -0.830462992 -1.573211670 0.894630790 0.062033433 0.780822515
## [1991] -0.045784928 -0.686705172 -0.632795990 -0.980210721 -0.722644627
## [1996] -1.609151125 -2.393829107 -2.136263132 -0.309340924 0.349549055
## [2001] 0.583155513 0.702953696 0.828741789 0.163861886 -1.441433668
## [2006] -1.818797946 -0.944271266 -0.231472105 1.271995068 -1.489352942
## [2011] -1.177877665 -1.663060308 -1.393514395 -0.938281357 -2.082353830
## [2016] -1.704989672 -1.333615303 -0.896351993 -1.681030035 -0.890362084
## [2021] -1.435443759 -0.704674900 1.805096865 1.038388610 2.200430870
## [2026] 2.008753777 2.266319990 -1.367688298 -1.205458879 1.672804713
## [2031] -1.147893667 -1.467119217 -0.336746663 -1.901475430 -1.331055880
## [2036] -0.106485561 -1.273490667 -1.472352505 -1.508984923 -0.263481766
## [2041] 0.756993532 1.269847751 1.599539757 1.379745126 0.866890848
## [2046] -1.776868582 -0.057764746 0.792802334 -0.279391378 1.086307883
## [2051] 1.445702434 0.541226149 -1.375544667 -1.657070398 -0.009845474
## [2056] -0.572896898 -0.489038169 -0.692695081 -0.656755626 -1.315645576
## [2061] -0.626806080 -1.872707129 -0.650765717 -0.327310652 -0.596856534
## [2066] -1.285696030 -1.447423577 0.020104071 1.395444751 0.605230510
## [2071] 0.688961804 -0.200683281 -0.383845508 -0.907166183 -0.525142074
## [2076] -1.603897572 1.429544926 -2.365957499 -1.959032297 -1.862849951
## [2081] -1.159907937 -0.291371197 0.870671153 -1.100008845 1.655349135
## [2086] -0.962240994 -0.986200631 -0.866402447 -0.908331811 -1.794838309
## [2091] -0.512997806 -0.710664809 -0.453098714 -0.956251085 -1.848747492
## [2096] -1.279706120 -0.998180449 -0.435129017 -2.357889652 0.934922516
## [2101] 1.327413082 -0.551308095 -1.242091417 1.233215332 -0.781569183
## [2106] 1.667571425 0.950622141 1.725136757 1.458243132 2.070528269
## [2111] 2.237990856 2.410686731 2.426386356 -1.252557755 -1.399087548
## [2116] -1.294423461 -1.561316967 -1.665981054 -1.686913967 -0.875766933
## [2121] -1.728779554 -1.849143386 -0.451877207 1.871666551 2.149026394
## [2126] 1.578606963 2.337421894 0.861657619 -0.109372213 0.134782910
## [2131] -2.247579098 1.954848409 -1.137783170 -0.545892000 -2.224677086
## [2136] -2.372247934 -1.339252472 -1.276007891 -0.826268375 -1.985753059
## [2141] -2.506531715 1.229781628 2.199003458 -2.484335661 1.200187087
## [2146] -2.425146580 1.496132731 -1.463323355 -1.130384564 1.074410200
## [2151] -1.648289323 -1.219168186 -1.911941767 2.138560057 -1.822977304
## [2156] -0.844367683 1.196582913 1.569999933 -1.557095051 -0.882485807
## [2161] -2.442519665 -1.037083745 -1.212763309 -1.774937630 0.002938857
## [2166] -2.618199348 -1.831155062 -1.121409893 -0.987893522 -2.323057652
## [2171] -1.873318195 -2.899286509 2.162010193 2.679915190 3.412380457
## [2176] 2.272989988 2.213800669 1.710693240 2.176807642 2.687313795
## [2181] 2.243395329 1.932652473 1.843868732 2.051030636 1.030018330
## [2186] -2.580518007 -2.070011854 -1.433728814 -2.077410460 -1.922039032
## [2191] -1.803660750 -2.151396990 -1.611296177 -1.855451226 -1.063796759
## [2196] -1.700079799 -2.550923347 0.563364863 -1.707846761 -0.587940574
## [2201] -1.320589423 -1.179292917 -1.697380304 -0.975197852 -1.650281429
## [2206] -1.043229580 0.217973247 -2.166212559 2.553835630 1.715248227
## [2211] 1.571490407 2.404087782 2.410077810 1.649359226 -1.465393305
## [2216] 0.984479368 0.924580336 1.349863887 0.948539913 -0.147613376
## [2221] 0.786812425 -1.231786847 -0.261421651 -1.998495221 0.834731698
## [2226] -2.292000771 0.852701426 1.661339045 1.511591315 1.541540861
## [2231] 0.433407784 -1.387524486 -0.285381287 -0.471068442 -1.028129935
## [2236] -0.716654718 -0.195532650 -0.950261176 -2.196162224 -0.842442811
## [2241] -1.603161216 1.437310338 1.484409213 0.783159554 1.861200094
## [2246] 1.055286288 -1.676447511 1.227982044 -0.530375302 1.337879419
## [2251] 0.673262179 -1.566550136 -1.852236629 -1.198708892 -1.254926324
## [2256] -2.386302233 -1.423578620 -2.632253647 -2.702525377 -2.962531090
## [2261] -1.944234967 -1.877647161 1.259376168 -2.410349369 1.621909618
## [2266] -1.766667604 -2.232781887 -1.633492112 -2.040417194 -1.441127419
## [2271] -2.469538450 2.731705666 1.614510894 1.681098700 1.362957120
## [2276] 1.303768039 -1.606285334 -1.620339751 -2.674416780 -2.400356770
## [2281] -2.344139338 -2.301976204 -2.779824495 -1.929535627 -2.203595638
## [2286] -2.379275084 -1.543040752 -2.625226498 -1.592231035 -2.091160774
## [2291] -1.753856182 -2.140351057 -2.583063364 -2.034943342 1.194126248
## [2296] 0.966509640 0.445387602 -0.680715263 -0.495028079 -1.962555766
## [2301] -2.232101679 0.690973878 0.577165604 1.044378519 2.128551960
## [2306] -1.802044511 1.552440882 0.594764113 -1.891008973 -0.462343603
## [2311] 1.918765306 1.112851501 1.756536007 -1.885775805 -0.713537514
## [2316] -0.954264998 -2.284572363 -1.019404888 -1.418931484 -2.462139845
## [2321] -1.114382744 -1.521959186 -1.803046346 -1.648448467 -2.189541340
## [2326] -2.735889435 -2.099606514 -0.975013077 -2.846868992 -2.891260862
## [2331] -1.182175040 -2.180991411 -2.610112667 -2.217984676 -2.706295013
## [2336] -2.595315218 1.740287781 -2.624909878 -2.543524742 -1.159979105
## [2341] -1.278357387 -1.500316501 -2.055214643 -2.590090513 -2.210622787
## [2346] -2.597117662 -2.112242222 -1.163573027 -1.346279740 -2.013861895
## [2351] -2.105215073 1.794869781 -2.800905943 -2.660362244 -2.807933092
## [2356] -2.817274570 -2.795078516 -0.871432126 -2.107005119 -2.661903143
## [2361] -3.105821371 -1.115587234 -1.626093388 -3.002240419 -3.091024160
## [2366] -2.526845932 -1.191681743 -1.746828914 -1.781964898 -1.914640427
## [2371] -1.189857483 -0.644775808 -1.165897846 -0.800513446 -1.070059299
## [2376] -1.105998755 -1.064069390 -1.034119844 -0.878382266 -1.076049209
## [2381] -1.716969490 -1.519302487 -0.854422629 -2.447738409 0.319599509
## [2386] -0.614106596 -0.232082516 -0.509442449 5.828471184 3.811670303
## [2391] 3.094897985 -2.691497564 -1.907241821 -1.596498847 -1.034202218
## [2396] -1.396735549 -1.167377710 -2.280894518 -2.832071781 -2.728490829
## [2401] -1.751870275 -1.100794792 -1.461886048 -1.812510848 -1.001363873
## [2406] -1.158360124 -2.561546564 -1.082039118 -1.249756575 -0.345280379
## [2411] -0.507007897 -2.214131832 -1.423463941 -1.459403396 -0.530967534
## [2416] -0.333300561 -1.453413486 -0.189542741 -1.046099663 -0.399189562
## [2421] -2.591496229 -1.621130943 0.918590426 -1.465741754 -1.233844757
## [2426] -2.537586927 -1.531282306 -1.183867574 -1.363564849 -1.147928119
## [2431] -1.111988664 -1.213817120 -1.135948300 -1.567221761 -1.357574940
## [2436] -1.309655666 -0.518987715 -2.202152014 -1.483363032
# See all distinct values in phonecallraw
unique(performance_labeled_p2$phonecallraw)
## [1] 223 499 649 709 403 760 268 73 301 629 529 662 614 514 591
## [16] 412 493 488 442 587 457 503 463 522 246 337 435 382 199 501
## [31] 480 492 470 228 235 489 560 376 656 195 89 413 383 309 404
## [46] 433 270 300 701 429 126 295 613 711 354 661 446 434 635 704
## [61] 623 617 785 563 647 548 784 564 650 646 673 753 606 654 541
## [76] 727 734 607 755 579 NA 546 634 643 651 585 348 624 517 506
## [91] 6 358 465 498 574 565 170 638 616 497 159 612 421 214 116
## [106] 474 537 3 450 456 415 362 625 601 619 144 531 453 572 249
## [121] 158 247 273 519 386 423 417 357 381 82 521 545 201 329 365
## [136] 347 72 233 239 285 343 397 394 167 299 335 520 7 462 304
## [151] 331 400 390 536 472 427 388 389 487 145 511 684 702 356 204
## [166] 690 782 231 508 569 683 791 464 765 500 678 533 321 523 471
## [181] 598 416 513 524 366 798 482 671 551 528 419 481 483 641 561
## [196] 466 583 444 232 552 622 608 620 640 648 823 645 695 751 584
## [211] 380 459 831 805 780 777 688 256 933 828 826 1041 839 507 872
## [226] 799 1141 816 659 871 680 686 667 556 698 408 554 570 349 895
## [241] 544 633 525 420 590 627 728 697 293 748 717 689 891 691 1024
## [256] 768 923 712 676 679 577 776 985 312 800 924 555 325 109 94
## [271] 504 445 567 458 332 426 494 351 594 596 535 378 125 369 543
## [286] 553 266 440 518 292 377 205 825 739 597 340 112 763 582 414
## [301] 530 550 566 96 184 663 478 288 422 496 432 639 346 452 156
## [316] 398 359 451 475 92 155 485 502 630 438 527 473 491 317 773
## [331] 611 593 599 578 479 302 70 766 830 515 161 2 243 665 767
## [346] 858 637 708 808 722 516 540 588 425 538 98 363 265 374 1
## [361] 166 330 207 441 260 280 399 576 512 592 685 595 642 600 431
## [376] 681 743 636 747 119 752 863 729 573 660 868 715 694 829 822
## [391] 539 395 526 644 779 603 385 559 495 557 294 88 586 104 769
## [406] 448 562 1084 880 756 795 703 874 128 221 164 549 571 632 631
## [421] 618 575 286 418 364 469 192 183 430 460 558 437 505 424 401
## [436] 379 790 732 710 738 274 762 771 626 151 666 534 653 759 806
## [451] 833 484 733 71 664 510 350 60 333 318 352 353 291 241 306
## [466] 279 323 407 387 355 328 271 296 315 245 240 310 290 262 250
## [481] 344 411 393 282 371 373 220 368 610 367 674 202 44 361 190
## [496] 287 67 876 461 428 143 477 406 242 278 129 372 375 628 726
## [511] 668 731 370 532 696 568 225 672 775 509 314 580 725 277 12
## [526] 59 341 180 197 86 238 48 339 410 4 252 476 140 455 322
## [541] 63 308 402 384 103 111 326 19 193 289 146 391 447 244 345
## [556] 307 81 313 443 468 449 338 284 602 162 713 467 327 212 298
## [571] 615 700 655 621 714 609 547 253 589 230 707 342 658 173 409
## [586] 186 102 320 405 45 336 258 74 454 105 139 206 486 749 605
## [601] 257 334 846 720 938 928 38 581 778 761 490 817 677 744 730
## [616] 803 670 264 84 789 148 772 716 211 188 319 152 99 108 276
## [631] 275 392 227 234 259 360 15 236 176 149 154 316 47 16 21
## [646] 100 55 324 283 65 297 311 118 216 255 123 210 237 251 305
## [661] 122 77 217 75 79 396 147 269 860 750 877 254 171 436 542
## [676] 261 222 281 31 157 137 208 165 194 97 182 107 132 110 22
## [691] 90 136 114 120 179 263 226 203 213 741 267 692 792 834 810
## [706] 910 802 824 181 87 742 737 764 966 949 941 13 705 28 706
## [721] 801 652 218 168 439 604 187 757 177 841 807 786 657 774 215
## [736] 770 735 303 224 248 229 160 142 682 219 191 113 23 189 272
## [751] 196 138 64 106 18 124 175 687 788 115 51 57 153 669 172
## [766] 758 835 178 209 76 185 745 101 169 693 675 718 867 900 903
## [781] 200 141 121 117 797 850 886 85 93 198 848 91 62 174 783
## [796] 882 724 130 95 883 719 859 723 50 54 39 61 812 736 78
## [811] 36 30 68 66 134 135 56 35 40 43 1264 977 875 163 52
## [826] 29 24 33
# See all distinct values in WFH_due_building_issues
unique(performance_labeled_p2$WFH_due_building_issues)
## [1] 0 1
Converted the variable WFH_due_building_issues into
interpretable factor labels (Remote for 1 and
On-site for 0).
performance_labeled_p2$WFH_due_building_issues <- factor(dplyr::recode(performance_labeled_p2$WFH_due_building_issues,
`1` = "Remote",
`0` = "On-site"))
Extracted the year and week components from the
year_week variable and removed the original field.
## Transform into weekly data
performance_labeled_p2 <- performance_labeled_p2 %>% mutate(year=as.numeric(substr(year_week, 1, 4)))
performance_labeled_p2 <- performance_labeled_p2 %>%
mutate(week = as.numeric(substr(year_week, 5, 6)))
performance_labeled_p2 <- performance_labeled_p2 %>% dplyr::select(-year_week)
performance_labeled_p2$date <- ISOweek::ISOweek2date(paste0(performance_labeled_p2$year, "-W",
sprintf("%02d", performance_labeled_p2$week), "-1"))
Repositioned the columns and reassigned the dataset to
performance_clean.
performance_clean <- performance_labeled_p2 %>%
dplyr::select(personid, date, year, week, perform1, phonecall, phonecallraw, WFH_due_building_issues,
logphonecall, logcallpersec, logcalllength, logcall_dayworked, everything())
rm(performance_labeled_p2)
##The Summary Volunteer Dataset
The summary_volunteer_labeled_p2
dataset contains demographic and background information about
participants, including age, tenure, commute distance, gender, marital
status, education, bedroom availability, and presence of children.
The following cleaning steps were performed:
The following cleaning steps were performed for the summary (demographic) dataset:
View(),
skim(), and head() to understand its structure
and variable types.personid, age,
tenure, commute, men,
married, high_educ, bedroom, and
children.men to
gender to improve clarity and consistency.personid, age, tenure,
commute, gender, married,
higher_edu_indicator, bedroom_indicator,
children_indicator) appear at the front of the
dataset.summary_clean for subsequent analysis.# Viewing and analysing the data
# View(summary_volunteer_labeled_p2)
skim(summary_volunteer_labeled_p2)
| Name | summary_volunteer_labeled… |
| Number of rows | 135 |
| Number of columns | 9 |
| _______________________ | |
| Column type frequency: | |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| personid | 0 | 1 | 32716.79 | 10835.70 | 4122 | 25913 | 37292 | 40464.0 | 45442 | ▁▂▂▃▇ |
| age | 0 | 1 | 23.84 | 3.35 | 18 | 22 | 23 | 25.5 | 35 | ▃▇▅▁▁ |
| tenure | 0 | 1 | 21.41 | 19.05 | 2 | 8 | 13 | 31.0 | 94 | ▇▃▂▁▁ |
| children | 0 | 1 | 0.15 | 0.36 | 0 | 0 | 0 | 0.0 | 1 | ▇▁▁▁▂ |
| bedroom | 0 | 1 | 0.98 | 0.15 | 0 | 1 | 1 | 1.0 | 1 | ▁▁▁▁▇ |
| commute | 0 | 1 | 105.48 | 66.90 | 10 | 50 | 90 | 167.5 | 300 | ▇▅▅▂▁ |
| men | 0 | 1 | 0.50 | 0.50 | 0 | 0 | 1 | 1.0 | 1 | ▇▁▁▁▇ |
| married | 0 | 1 | 0.21 | 0.41 | 0 | 0 | 0 | 0.0 | 1 | ▇▁▁▁▂ |
| high_educ | 0 | 1 | 0.41 | 0.49 | 0 | 0 | 0 | 1.0 | 1 | ▇▁▁▁▆ |
head(summary_volunteer_labeled_p2)
## # A tibble: 6 × 9
## personid age tenure children bedroom commute men married high_educ
## <dbl> <dbl> <dbl> <dbl+lbl> <dbl+lbl> <dbl> <dbl> <dbl> <dbl>
## 1 4122 30 94 0 [no] 0 [no] 180 0 0 0
## 2 6278 32 77 1 [yes] 1 [yes] 170 0 1 1
## 3 7720 25 70 0 [no] 1 [yes] 180 0 0 1
## 4 8834 22 66 0 [no] 0 [no] 60 0 0 0
## 5 8854 22 66 0 [no] 1 [yes] 60 0 0 0
## 6 10098 28 63 1 [yes] 1 [yes] 180 0 1 1
Checked for missing values, duplicate rows, and examined distinct
values of key variables (personid, age,
tenure, commute, men,
married, high_educ, bedroom,
children).
# Check for missing values
colSums(is.na(summary_volunteer_labeled_p2))
## personid age tenure children bedroom commute men married
## 0 0 0 0 0 0 0 0
## high_educ
## 0
# Check for duplicates
summary_volunteer_labeled_p2[duplicated(summary_volunteer_labeled_p2), ]
## # A tibble: 0 × 9
## # ℹ 9 variables: personid <dbl>, age <dbl>, tenure <dbl>, children <dbl+lbl>,
## # bedroom <dbl+lbl>, commute <dbl>, men <dbl>, married <dbl>, high_educ <dbl>
# Inspect distinct values
unique(summary_volunteer_labeled_p2$personid)
## [1] 4122 6278 7720 8834 8854 10098 10356 12426 12974 13980 14048 14220
## [13] 14522 14528 15444 16334 16422 16424 16514 16594 16596 17160 17906 19470
## [25] 21654 22284 23136 23228 23772 24324 24608 25520 25638 25864 25962 26328
## [37] 26634 26934 27704 28190 28224 28484 29172 29230 29808 30014 31136 31150
## [49] 31292 31888 31936 32320 32804 33128 33278 33350 33354 34890 35006 35344
## [61] 35822 36032 36288 36314 36494 36908 37276 37292 37294 37798 38038 38290
## [73] 38552 38566 38580 38712 38842 38862 38878 38898 39096 39144 39164 39458
## [85] 39466 39478 39634 39942 39990 40008 40034 40062 40162 40174 40192 40316
## [97] 40322 40328 40336 40346 40456 40472 40490 41286 41320 41332 42096 42104
## [109] 42108 42152 42308 42592 42618 42624 42628 42632 42634 42682 43258 43264
## [121] 43288 43524 43534 43570 43926 44256 44266 44282 44408 44782 44784 44794
## [133] 44800 45254 45442
unique(summary_volunteer_labeled_p2$age)
## [1] 30 32 25 22 28 27 29 34 21 23 24 26 20 35 19 18 31
unique(summary_volunteer_labeled_p2$tenure)
## [1] 94.0 77.0 70.0 66.0 63.0 62.0 56.0 54.0 51.0 50.0 49.0 47.0 46.0 45.0 42.0
## [16] 37.0 36.0 35.0 34.0 32.0 31.0 30.0 28.0 27.0 26.0 25.0 24.0 23.0 22.0 21.0
## [31] 20.0 19.0 18.0 15.0 13.0 12.0 11.0 10.0 9.0 8.0 6.0 5.0 4.0 3.0 3.5
## [46] 2.5 2.0
unique(summary_volunteer_labeled_p2$commute)
## [1] 180 170 60 20 120 240 40 65 90 70 160 210 80 45 35 30 32 50 140
## [20] 135 75 200 10 300 150 28 100 165 190 105 55
unique(summary_volunteer_labeled_p2$men)
## [1] 0 1
unique(summary_volunteer_labeled_p2$married)
## [1] 0 1
unique(summary_volunteer_labeled_p2$high_educ)
## [1] 0 1
unique(summary_volunteer_labeled_p2$bedroom)
## <labelled<double>[2]>: number of bedrooms at home
## [1] 0 1
##
## Labels:
## value label
## 0 no
## 1 yes
unique(summary_volunteer_labeled_p2$children)
## <labelled<double>[2]>: number of children
## [1] 0 1
##
## Labels:
## value label
## 0 no
## 1 yes
Renamed the variable men to gender for
clarity and consistency.
# Rename men to gender
summary_volunteer_labeled_p2 <- summary_volunteer_labeled_p2 %>%
rename(gender = men)
Converted binary indicators into interpretable factor labels for
gender, marital_status,
higher_edu_indicator, bedroom_indicator, and
children_indicator. Cleaned and formatted categorical
variables by applying descriptive labels to all binary demographic
indicators.
# Clean and format variables
summary_clean <- summary_volunteer_labeled_p2 %>%
mutate(
gender = factor(gender, levels = c(0, 1),
labels = c("Female", "Male")),
marital_status = factor(married, levels = c(0, 1),
labels = c("Not Married", "Married")),
higher_edu_indicator = factor(high_educ, levels = c(0, 1),
labels = c("No Degree", "Has Degree")),
bedroom_indicator = factor(bedroom, levels = c(0, 1),
labels = c("No Bedroom", "Has Own Bedroom")),
children_indicator = factor(children, levels = c(0, 1),
labels = c("No Children", "Has Child/Children"))
)
Selected and reordered columns to place the key demographic variables at the front of the cleaned dataset.
summary_clean <- summary_clean %>%
dplyr::select(personid, age, tenure, commute,
gender, married, higher_edu_indicator,
bedroom_indicator, children_indicator)
Assigned the cleaned output as summary_clean for further
analysis.
# Remove original dataset
rm(summary_volunteer_labeled_p2)
The wage_new_labeled_p2 dataset contains monthly wage
information, including base wage, bonus payments, gross wage, and the
corresponding wage month.
The following cleaning steps were performed:
The following cleaning steps were performed for the wage dataset:
View(),
skim(), and head() to understand its structure
and variable types.wage_month from
YYYYMM format into a proper date variable
(wage_month_date) representing the first day of each
month.basewage and
bonustotal to ensure accurate wage values.basewage,
bonustotal, grosswage) were correctly
formatted as numeric values.personid, wage_month,
wage_month_date, basewage,
bonustotal, grosswage) appear at the front of
the dataset.wage_clean for further analysis.wage_new_labeled_p2 from
the environment to prevent conflicts during later merging.Inspected the dataset using View(), skim(),
and head() to understand structure and data types.
# Viewing and analysing the data
# View(wage_new_labeled_p2)
skim(wage_new_labeled_p2)
| Name | wage_new_labeled_p2 |
| Number of rows | 3007 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| wage_month | 0 | 1 | 6 | 6 | 0 | 26 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| basewage | 0 | 1 | 1665.83 | 221.26 | 650.00 | 1500.00 | 1600.00 | 1800.00 | 2850 | ▁▃▇▁▁ |
| grosswage | 0 | 1 | 3133.63 | 1064.47 | 48.85 | 2455.24 | 2978.72 | 3727.00 | 14553 | ▇▇▁▁▁ |
| personid | 0 | 1 | 32285.38 | 10849.71 | 4122.00 | 25520.00 | 36908.00 | 40336.00 | 45442 | ▁▂▂▃▇ |
| bonustotal | 0 | 1 | 1502.65 | 927.90 | 0.00 | 914.50 | 1325.17 | 1964.74 | 12853 | ▇▁▁▁▁ |
head(wage_new_labeled_p2)
## # A tibble: 6 × 5
## basewage grosswage personid wage_month bonustotal
## <dbl> <dbl> <dbl> <chr> <dbl>
## 1 1650 3651. 4122 202201 2001.
## 2 1650 4776. 4122 202202 3126.
## 3 1650 4358 4122 202203 2708
## 4 1650 4801 4122 202204 3151
## 5 1650 4045. 4122 202205 2395.
## 6 1650 5498. 4122 202206 3848.
Converted the wage_month variable from a
YYYYMM character format into a proper date variable
(wage_month_date) representing the first day of each
month.
# Casting the data type from character to date for wage_month
wage_clean <- wage_new_labeled_p2 %>%
mutate(
wage_month_date = as.Date(paste0(wage_month, "01"), format = "%Y%m%d")
)
Identified inconsistencies between basewage,
bonustotal, and grosswage, and recalculated
grosswage to ensure accuracy.
# Recalculate gross wage because of inconsistencies
wage_clean <- wage_clean %>%
mutate(
grosswage = basewage + bonustotal
)
Reorganized the dataset by placing key variables at the front
(personid, wage_month,
wage_month_date, basewage,
bonustotal, grosswage) and assigned the
cleaned output as wage_clean for further analysis.
# Adjusting column positions
wage_clean <- wage_clean %>%
dplyr::select(personid, wage_month, wage_month_date, basewage,
bonustotal, grosswage, everything())
Removed the original dataset (wage_new_labeled_p2) from
the environment to avoid conflicts during merging.
rm(wage_new_labeled_p2)
#Data Merging
The goal of this step is to construct a final person-level dataset by
aggregating weekly or monthly information to the individual level and
merging all sources using personid.
The following aggregation and merging steps were performed:
The following cleaning steps were performed for the summary volunteer dataset:
View(),
skim(), and head() to understand its structure
and data types.personid,
age, tenure, commute,
gender, married, high_educ,
bedroom, and children.men to
gender for improved clarity and consistency.personid, age, tenure,
commute, gender, married,
higher_edu_indicator, bedroom_indicator,
children_indicator) appear at the front of the
dataset.summary_clean for further analysis.Aggregating attitude data to the person level
# Aggregate weekly attitude variables to person-level means
attitude_agg <- attitude_clean %>%
group_by(personid) %>%
summarise(
exhaustion = mean(exhaustion, na.rm = TRUE), # Average weekly exhaustion
negative = mean(negative, na.rm = TRUE), # Average weekly negative affect
positive = mean(positive, na.rm = TRUE) # Average weekly positive affect
)
# Convert continuous averages to integer scale (rounded)
attitude_agg$exhaustion <- as.integer(round(attitude_agg$exhaustion))
attitude_agg$negative <- as.integer(round(attitude_agg$negative))
attitude_agg$positive <- as.integer(round(attitude_agg$positive))
Aggregating weekly performance data
# Aggregate weekly performance into person-level metrics
performance_agg_person <- performance_clean %>%
group_by(personid) %>%
summarise(
mean_overall_perf_z_score = mean(perform1, na.rm = TRUE), # Avg z-score performance
mean_phonecall_perf_z_score = mean(phonecall, na.rm = TRUE), # Avg phonecall z-score
total_calls = sum(phonecallraw, na.rm = TRUE), # Total call volume
# Log-transformed performance metrics (averages)
mean_log_phone_calls = mean(logphonecall, na.rm = TRUE),
mean_log_calls_per_second = mean(logcallpersec, na.rm = TRUE),
mean_log_average_call_lenght= mean(logcalllength, na.rm = TRUE),
mean_log_calls_per_day_worked = mean(logcall_dayworked, na.rm = TRUE),
mean_log_days_worked = mean(logdaysworked, na.rm = TRUE),
# Work format counts
weeks_observed = n(),
remote_weeks = sum(WFH_due_building_issues == "Remote", na.rm = TRUE),
on_site_weeks = sum(WFH_due_building_issues == "On-site", na.rm = TRUE),
# Work format proportions
pct_remote_weeks = remote_weeks / weeks_observed,
pct_on_site_weeks = on_site_weeks / weeks_observed
) %>%
ungroup()
Aggregating wage data
wage_medians <- wage_clean %>%
group_by(personid) %>% # Group by person ID
summarise(
median_base_wage_cny = median(basewage, na.rm = TRUE), # Median base monthly wage
median_gross_wage_cny = median(grosswage, na.rm = TRUE), # Median gross monthly wage
median_bonus_total_cny = median(bonustotal, na.rm = TRUE), # Median total bonus
months_observed = n() # Number of months observed
) %>%
ungroup() # Remove grouping
Preparing summary and endperiod data
# Keep unique demographic entries per person
summary_agg <- summary_clean %>%
dplyr::select(personid, everything()) %>%
distinct(personid, .keep_all = TRUE)
# Keep unique end-period outcome entries
endperiod_agg <- endperiod_clean %>%
dplyr::select(personid, everything()) %>%
distinct(personid, .keep_all = TRUE)
Creating final_all person-level dataset
# Sequentially merge: demographics → outcomes → performance → wage → attitudes
final_all <- summary_agg %>%
left_join(endperiod_agg, by = "personid") %>%
left_join(performance_agg_person, by = "personid") %>%
left_join(wage_medians, by = "personid") %>%
left_join(attitude_agg, by = "personid") %>%
distinct(personid, .keep_all = TRUE) # Ensure unique persons
In addition to the final_all person-level dataset, a
weekly panel dataset final_panel_weekly was constructed
that combines outcomes, demographics, attitudes, performance, and wages
at the week level.
The following steps were performed:
In addition to the person-level file, a weekly panel dataset was
constructed that combines outcomes, demographics, attitudes,
performance, and wages at the week level.
The following steps were performed:
endperiod_clean and
summary_clean contained the same employees based on
personid and merged them into
aa_sum_end_join.att_perf_join via a full join of
attitude_clean and performance_clean on
personid, year, and week, and
resolved duplicate date fields using
coalesce().wage_clean for weekly merging
by constructing test_wage_new_labeled_p2 with a proper
monthly date variable and a common month key,
and then joined wages to att_perf_join by
personid and month, yielding
att_with_wage.att_with_wage, confirming stable summary statistics and a
low share of missing wage values.final_panel_weekly by joining att_with_wage
with aa_sum_end_join on personid, and removed
intermediate objects before inspecting final_all and
final_panel_weekly with skim() and
head().Checked whether both datasets contained the same
personid values and merged them into
aa_sum_end_join, followed by an inspection with
skim().
# Check if both datasets contain the same employees
sort(unique(endperiod_clean$personid)) == sort(unique(summary_clean$personid))
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
# Merge them
aa_sum_end_join <- left_join(endperiod_clean,
summary_clean,
by = "personid")
skimr::skim(aa_sum_end_join)
| Name | aa_sum_end_join |
| Number of rows | 135 |
| Number of columns | 12 |
| _______________________ | |
| Column type frequency: | |
| factor | 4 |
| numeric | 8 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| gender | 0 | 1 | FALSE | 2 | Mal: 68, Fem: 67 |
| higher_edu_indicator | 0 | 1 | FALSE | 2 | No : 79, Has: 56 |
| bedroom_indicator | 0 | 1 | FALSE | 2 | Has: 132, No : 3 |
| children_indicator | 0 | 1 | FALSE | 2 | No : 115, Has: 20 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| personid | 0 | 1 | 32716.79 | 10835.70 | 4122 | 25913.0 | 37292 | 40464.0 | 45442 | ▁▂▂▃▇ |
| promote_switch | 0 | 1 | 0.16 | 0.37 | 0 | 0.0 | 0 | 0.0 | 1 | ▇▁▁▁▂ |
| quitjob | 0 | 1 | 0.30 | 0.46 | 0 | 0.0 | 0 | 1.0 | 1 | ▇▁▁▁▃ |
| costofcommute | 0 | 1 | 7.40 | 7.22 | 0 | 2.5 | 6 | 10.0 | 55 | ▇▂▁▁▁ |
| age | 0 | 1 | 23.84 | 3.35 | 18 | 22.0 | 23 | 25.5 | 35 | ▃▇▅▁▁ |
| tenure | 0 | 1 | 21.41 | 19.05 | 2 | 8.0 | 13 | 31.0 | 94 | ▇▃▂▁▁ |
| commute | 0 | 1 | 105.48 | 66.90 | 10 | 50.0 | 90 | 167.5 | 300 | ▇▅▅▂▁ |
| married | 0 | 1 | 0.21 | 0.41 | 0 | 0.0 | 0 | 0.0 | 1 | ▇▁▁▁▂ |
Merged weekly attitude and performance data using a full join on
personid, year, and week,
resolved duplicate date fields, and inspected the result with
skim().
# Full join because the time frames differ slightly
att_perf_join <- full_join(attitude_clean,
performance_clean,
by = c("personid", "year", "week"))
# Fix date variable (from attitude or performance)
att_perf_join <- att_perf_join %>%
mutate(date = coalesce(date.x, date.y)) %>% # take non-NA value
dplyr::select(-date.x, -date.y)
skimr::skim(att_perf_join)
| Name | att_perf_join |
| Number of rows | 10079 |
| Number of columns | 16 |
| _______________________ | |
| Column type frequency: | |
| Date | 1 |
| factor | 1 |
| numeric | 14 |
| ________________________ | |
| Group variables | None |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| date | 0 | 1 | 2022-01-03 | 2023-08-28 | 2022-10-17 | 87 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| WFH_due_building_issues | 209 | 0.98 | FALSE | 2 | On-: 7932, Rem: 1938 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| personid | 0 | 1.00 | 32518.87 | 10613.45 | 4122.00 | 25864.00 | 36908.00 | 40328.00 | 45442.00 | ▁▂▂▃▇ |
| year | 0 | 1.00 | 2022.35 | 0.48 | 2022.00 | 2022.00 | 2022.00 | 2023.00 | 2023.00 | ▇▁▁▁▅ |
| week | 0 | 1.00 | 23.91 | 14.49 | 1.00 | 12.00 | 23.00 | 34.00 | 53.00 | ▇▇▇▅▅ |
| exhaustion | 7700 | 0.24 | 8.61 | 7.79 | 0.00 | 2.00 | 7.00 | 12.00 | 36.00 | ▇▅▂▁▁ |
| negative | 7700 | 0.24 | 16.65 | 6.85 | 8.00 | 11.00 | 16.00 | 21.00 | 40.00 | ▇▇▅▁▁ |
| positive | 7700 | 0.24 | 24.21 | 6.67 | 8.00 | 20.00 | 24.00 | 29.00 | 40.00 | ▁▃▇▅▁ |
| perform1 | 232 | 0.98 | -0.02 | 0.99 | -3.03 | -0.61 | 0.05 | 0.61 | 4.16 | ▁▆▇▁▁ |
| phonecall | 343 | 0.97 | -0.01 | 0.96 | -3.11 | -0.54 | 0.07 | 0.61 | 5.83 | ▁▇▃▁▁ |
| phonecallraw | 490 | 0.95 | 440.21 | 142.53 | 1.00 | 357.00 | 445.00 | 527.00 | 1264.00 | ▁▇▃▁▁ |
| logphonecall | 490 | 0.95 | 6.01 | 0.48 | 0.00 | 5.88 | 6.10 | 6.27 | 7.14 | ▁▁▁▁▇ |
| logcallpersec | 488 | 0.95 | -5.17 | 0.16 | -5.95 | -5.26 | -5.17 | -5.08 | -1.10 | ▇▁▁▁▁ |
| logcalllength | 488 | 0.95 | 11.17 | 0.52 | 2.48 | 11.05 | 11.27 | 11.44 | 12.12 | ▁▁▁▁▇ |
| logcall_dayworked | 488 | 0.95 | 9.47 | 0.46 | 2.48 | 9.33 | 9.55 | 9.73 | 10.36 | ▁▁▁▁▇ |
| logdaysworked | 209 | 0.98 | 1.69 | 0.28 | 0.00 | 1.61 | 1.79 | 1.79 | 1.95 | ▁▁▁▁▇ |
Converted wage_month into a proper monthly date, created
test_wage_new_labeled_p2 for merging, and inspected the
output with skim().
# Convert wage month (YYYYMM) into a proper monthly date
test_wage_new_labeled_p2 <- wage_clean %>%
mutate(
year = as.integer(substr(wage_month, 1, 4)),
month = as.integer(substr(wage_month, 5, 6)),
date = as.Date(paste(year, month, 1, sep = "-")) # first day of month
) %>%
dplyr::select(-year, -month)
skimr::skim(test_wage_new_labeled_p2)
| Name | test_wage_new_labeled_p2 |
| Number of rows | 3007 |
| Number of columns | 7 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| Date | 2 |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| wage_month | 0 | 1 | 6 | 6 | 0 | 26 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| wage_month_date | 0 | 1 | 2022-01-01 | 2024-02-01 | 2022-12-01 | 26 |
| date | 0 | 1 | 2022-01-01 | 2024-02-01 | 2022-12-01 | 26 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| personid | 0 | 1 | 32285.38 | 10849.71 | 4122.00 | 25520.00 | 36908.00 | 40336.00 | 45442 | ▁▂▂▃▇ |
| basewage | 0 | 1 | 1665.83 | 221.26 | 650.00 | 1500.00 | 1600.00 | 1800.00 | 2850 | ▁▃▇▁▁ |
| bonustotal | 0 | 1 | 1502.65 | 927.90 | 0.00 | 914.50 | 1325.17 | 1964.74 | 12853 | ▇▁▁▁▁ |
| grosswage | 0 | 1 | 3168.48 | 1020.03 | 1264.43 | 2472.15 | 2984.38 | 3733.81 | 14553 | ▇▂▁▁▁ |
Created monthly identifiers in both datasets, merged wage information
into the weekly data using personid and month,
and checked the result with skim().
# Create 'month' variable in both datasets
att_perf_join <- att_perf_join %>%
mutate(date = as.Date(date),
month = floor_date(date, unit = "month"))
test_wage_new_labeled_p2 <- test_wage_new_labeled_p2 %>%
mutate(date = as.Date(date),
month = floor_date(date, unit = "month"))
# Merge wage values for each person-month into weekly rows
att_with_wage <- att_perf_join %>%
left_join(
test_wage_new_labeled_p2 %>%
dplyr::select(personid, month, basewage, grosswage, bonustotal),
by = c("personid", "month")
)
skimr::skim(att_with_wage)
| Name | att_with_wage |
| Number of rows | 10079 |
| Number of columns | 20 |
| _______________________ | |
| Column type frequency: | |
| Date | 2 |
| factor | 1 |
| numeric | 17 |
| ________________________ | |
| Group variables | None |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| date | 0 | 1 | 2022-01-03 | 2023-08-28 | 2022-10-17 | 87 |
| month | 0 | 1 | 2022-01-01 | 2023-08-01 | 2022-10-01 | 20 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| WFH_due_building_issues | 209 | 0.98 | FALSE | 2 | On-: 7932, Rem: 1938 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| personid | 0 | 1.00 | 32518.87 | 10613.45 | 4122.00 | 25864.00 | 36908.00 | 40328.00 | 45442.00 | ▁▂▂▃▇ |
| year | 0 | 1.00 | 2022.35 | 0.48 | 2022.00 | 2022.00 | 2022.00 | 2023.00 | 2023.00 | ▇▁▁▁▅ |
| week | 0 | 1.00 | 23.91 | 14.49 | 1.00 | 12.00 | 23.00 | 34.00 | 53.00 | ▇▇▇▅▅ |
| exhaustion | 7700 | 0.24 | 8.61 | 7.79 | 0.00 | 2.00 | 7.00 | 12.00 | 36.00 | ▇▅▂▁▁ |
| negative | 7700 | 0.24 | 16.65 | 6.85 | 8.00 | 11.00 | 16.00 | 21.00 | 40.00 | ▇▇▅▁▁ |
| positive | 7700 | 0.24 | 24.21 | 6.67 | 8.00 | 20.00 | 24.00 | 29.00 | 40.00 | ▁▃▇▅▁ |
| perform1 | 232 | 0.98 | -0.02 | 0.99 | -3.03 | -0.61 | 0.05 | 0.61 | 4.16 | ▁▆▇▁▁ |
| phonecall | 343 | 0.97 | -0.01 | 0.96 | -3.11 | -0.54 | 0.07 | 0.61 | 5.83 | ▁▇▃▁▁ |
| phonecallraw | 490 | 0.95 | 440.21 | 142.53 | 1.00 | 357.00 | 445.00 | 527.00 | 1264.00 | ▁▇▃▁▁ |
| logphonecall | 490 | 0.95 | 6.01 | 0.48 | 0.00 | 5.88 | 6.10 | 6.27 | 7.14 | ▁▁▁▁▇ |
| logcallpersec | 488 | 0.95 | -5.17 | 0.16 | -5.95 | -5.26 | -5.17 | -5.08 | -1.10 | ▇▁▁▁▁ |
| logcalllength | 488 | 0.95 | 11.17 | 0.52 | 2.48 | 11.05 | 11.27 | 11.44 | 12.12 | ▁▁▁▁▇ |
| logcall_dayworked | 488 | 0.95 | 9.47 | 0.46 | 2.48 | 9.33 | 9.55 | 9.73 | 10.36 | ▁▁▁▁▇ |
| logdaysworked | 209 | 0.98 | 1.69 | 0.28 | 0.00 | 1.61 | 1.79 | 1.79 | 1.95 | ▁▁▁▁▇ |
| basewage | 12 | 1.00 | 1601.69 | 176.22 | 650.00 | 1500.00 | 1600.00 | 1700.00 | 2450.00 | ▁▁▇▂▁ |
| grosswage | 12 | 1.00 | 3095.24 | 988.18 | 1264.43 | 2453.00 | 2871.10 | 3539.66 | 14553.00 | ▇▂▁▁▁ |
| bonustotal | 12 | 1.00 | 1493.55 | 912.05 | 0.00 | 915.00 | 1284.43 | 1891.90 | 12853.00 | ▇▁▁▁▁ |
Merged weekly performance, attitude, and
wage data with summary and
endperiod data to create
final_panel_weekly.
# Combine (weekly performance+attitude+wage) with (demographics+endperiod)
final_panel_weekly <- full_join(att_with_wage,
aa_sum_end_join,
by = "personid")
Removed intermediate objects to clean the workspace and inspected the
final datasets using skim() and head().
# Remove unnecessary intermediate datasets
rm(attitude_agg, attitude_clean, attitude_performance, attitude_performance_wage,
endperiod_agg, endperiod_clean, performance_agg_person, performance_clean,
summary, summary_agg, summary_clean, volunteer_endperiod, wage_clean,
wage_medians, endperiod_outcomes, att_perf_join, att_with_wage,
test_wage_new_labeled_p2, aa_sum_end_join)
## Warning in rm(attitude_agg, attitude_clean, attitude_performance,
## attitude_performance_wage, : object 'attitude_performance' not found
## Warning in rm(attitude_agg, attitude_clean, attitude_performance,
## attitude_performance_wage, : object 'attitude_performance_wage' not found
## Warning in rm(attitude_agg, attitude_clean, attitude_performance,
## attitude_performance_wage, : object 'summary' not found
## Warning in rm(attitude_agg, attitude_clean, attitude_performance,
## attitude_performance_wage, : object 'volunteer_endperiod' not found
## Warning in rm(attitude_agg, attitude_clean, attitude_performance,
## attitude_performance_wage, : object 'endperiod_outcomes' not found
# Inspect merged datasets
skim(final_all)
| Name | final_all |
| Number of rows | 135 |
| Number of columns | 32 |
| _______________________ | |
| Column type frequency: | |
| factor | 4 |
| numeric | 28 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| gender | 0 | 1 | FALSE | 2 | Mal: 68, Fem: 67 |
| higher_edu_indicator | 0 | 1 | FALSE | 2 | No : 79, Has: 56 |
| bedroom_indicator | 0 | 1 | FALSE | 2 | Has: 132, No : 3 |
| children_indicator | 0 | 1 | FALSE | 2 | No : 115, Has: 20 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| personid | 0 | 1.00 | 32716.79 | 10835.70 | 4122.00 | 25913.00 | 37292.00 | 40464.00 | 45442.00 | ▁▂▂▃▇ |
| age | 0 | 1.00 | 23.84 | 3.35 | 18.00 | 22.00 | 23.00 | 25.50 | 35.00 | ▃▇▅▁▁ |
| tenure | 0 | 1.00 | 21.41 | 19.05 | 2.00 | 8.00 | 13.00 | 31.00 | 94.00 | ▇▃▂▁▁ |
| commute | 0 | 1.00 | 105.48 | 66.90 | 10.00 | 50.00 | 90.00 | 167.50 | 300.00 | ▇▅▅▂▁ |
| married | 0 | 1.00 | 0.21 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| promote_switch | 0 | 1.00 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| quitjob | 0 | 1.00 | 0.30 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| costofcommute | 0 | 1.00 | 7.40 | 7.22 | 0.00 | 2.50 | 6.00 | 10.00 | 55.00 | ▇▂▁▁▁ |
| mean_overall_perf_z_score | 0 | 1.00 | -0.06 | 0.54 | -1.20 | -0.40 | -0.04 | 0.28 | 1.42 | ▃▆▇▅▁ |
| mean_phonecall_perf_z_score | 0 | 1.00 | -0.04 | 0.49 | -1.12 | -0.34 | -0.04 | 0.27 | 1.22 | ▂▅▇▃▁ |
| total_calls | 0 | 1.00 | 31268.23 | 9635.28 | 6088.00 | 23760.00 | 33311.00 | 37984.50 | 49621.00 | ▁▃▅▇▃ |
| mean_log_phone_calls | 0 | 1.00 | 6.00 | 0.20 | 5.31 | 5.89 | 6.01 | 6.13 | 6.59 | ▁▃▇▆▁ |
| mean_log_calls_per_second | 1 | 0.99 | -5.17 | 0.12 | -5.56 | -5.24 | -5.17 | -5.10 | -4.81 | ▁▃▇▃▁ |
| mean_log_average_call_lenght | 1 | 0.99 | 11.16 | 0.20 | 10.45 | 11.06 | 11.17 | 11.29 | 11.67 | ▁▂▇▇▁ |
| mean_log_calls_per_day_worked | 1 | 0.99 | 9.47 | 0.20 | 8.67 | 9.38 | 9.48 | 9.60 | 9.91 | ▁▁▃▇▂ |
| mean_log_days_worked | 0 | 1.00 | 1.68 | 0.10 | 1.34 | 1.62 | 1.67 | 1.76 | 1.85 | ▁▂▇▆▇ |
| weeks_observed | 0 | 1.00 | 73.11 | 14.07 | 27.00 | 62.50 | 81.00 | 85.00 | 86.00 | ▁▁▂▃▇ |
| remote_weeks | 0 | 1.00 | 14.36 | 16.37 | 0.00 | 0.00 | 1.00 | 33.00 | 39.00 | ▇▁▁▂▅ |
| on_site_weeks | 0 | 1.00 | 58.76 | 16.79 | 26.00 | 47.00 | 55.00 | 72.00 | 86.00 | ▂▇▅▂▅ |
| pct_remote_weeks | 0 | 1.00 | 0.18 | 0.21 | 0.00 | 0.00 | 0.01 | 0.41 | 0.58 | ▇▁▁▅▁ |
| pct_on_site_weeks | 0 | 1.00 | 0.82 | 0.21 | 0.42 | 0.59 | 0.99 | 1.00 | 1.00 | ▁▅▁▁▇ |
| median_base_wage_cny | 0 | 1.00 | 1635.56 | 130.06 | 1300.00 | 1550.00 | 1600.00 | 1700.00 | 2375.00 | ▃▇▃▁▁ |
| median_gross_wage_cny | 0 | 1.00 | 3023.55 | 688.64 | 1919.76 | 2518.40 | 2792.00 | 3428.90 | 4801.74 | ▆▇▅▃▂ |
| median_bonus_total_cny | 0 | 1.00 | 1400.48 | 590.61 | 517.76 | 972.00 | 1208.60 | 1759.67 | 3051.98 | ▇▇▅▃▂ |
| months_observed | 0 | 1.00 | 22.27 | 5.05 | 7.00 | 19.50 | 26.00 | 26.00 | 26.00 | ▁▂▂▂▇ |
| exhaustion | 74 | 0.45 | 8.59 | 6.53 | 0.00 | 3.00 | 9.00 | 12.00 | 29.00 | ▇▇▅▁▁ |
| negative | 74 | 0.45 | 16.64 | 5.39 | 8.00 | 12.00 | 17.00 | 20.00 | 34.00 | ▇▆▇▁▁ |
| positive | 74 | 0.45 | 24.25 | 5.14 | 13.00 | 21.00 | 24.00 | 27.00 | 37.00 | ▂▆▇▅▁ |
skim(final_panel_weekly)
| Name | final_panel_weekly |
| Number of rows | 10079 |
| Number of columns | 31 |
| _______________________ | |
| Column type frequency: | |
| Date | 2 |
| factor | 5 |
| numeric | 24 |
| ________________________ | |
| Group variables | None |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| date | 0 | 1 | 2022-01-03 | 2023-08-28 | 2022-10-17 | 87 |
| month | 0 | 1 | 2022-01-01 | 2023-08-01 | 2022-10-01 | 20 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| WFH_due_building_issues | 209 | 0.98 | FALSE | 2 | On-: 7932, Rem: 1938 |
| gender | 0 | 1.00 | FALSE | 2 | Mal: 5064, Fem: 5015 |
| higher_edu_indicator | 0 | 1.00 | FALSE | 2 | No : 6051, Has: 4028 |
| bedroom_indicator | 0 | 1.00 | FALSE | 2 | Has: 9815, No : 264 |
| children_indicator | 0 | 1.00 | FALSE | 2 | No : 8658, Has: 1421 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| personid | 0 | 1.00 | 32518.87 | 10613.45 | 4122.00 | 25864.00 | 36908.00 | 40328.00 | 45442.00 | ▁▂▂▃▇ |
| year | 0 | 1.00 | 2022.35 | 0.48 | 2022.00 | 2022.00 | 2022.00 | 2023.00 | 2023.00 | ▇▁▁▁▅ |
| week | 0 | 1.00 | 23.91 | 14.49 | 1.00 | 12.00 | 23.00 | 34.00 | 53.00 | ▇▇▇▅▅ |
| exhaustion | 7700 | 0.24 | 8.61 | 7.79 | 0.00 | 2.00 | 7.00 | 12.00 | 36.00 | ▇▅▂▁▁ |
| negative | 7700 | 0.24 | 16.65 | 6.85 | 8.00 | 11.00 | 16.00 | 21.00 | 40.00 | ▇▇▅▁▁ |
| positive | 7700 | 0.24 | 24.21 | 6.67 | 8.00 | 20.00 | 24.00 | 29.00 | 40.00 | ▁▃▇▅▁ |
| perform1 | 232 | 0.98 | -0.02 | 0.99 | -3.03 | -0.61 | 0.05 | 0.61 | 4.16 | ▁▆▇▁▁ |
| phonecall | 343 | 0.97 | -0.01 | 0.96 | -3.11 | -0.54 | 0.07 | 0.61 | 5.83 | ▁▇▃▁▁ |
| phonecallraw | 490 | 0.95 | 440.21 | 142.53 | 1.00 | 357.00 | 445.00 | 527.00 | 1264.00 | ▁▇▃▁▁ |
| logphonecall | 490 | 0.95 | 6.01 | 0.48 | 0.00 | 5.88 | 6.10 | 6.27 | 7.14 | ▁▁▁▁▇ |
| logcallpersec | 488 | 0.95 | -5.17 | 0.16 | -5.95 | -5.26 | -5.17 | -5.08 | -1.10 | ▇▁▁▁▁ |
| logcalllength | 488 | 0.95 | 11.17 | 0.52 | 2.48 | 11.05 | 11.27 | 11.44 | 12.12 | ▁▁▁▁▇ |
| logcall_dayworked | 488 | 0.95 | 9.47 | 0.46 | 2.48 | 9.33 | 9.55 | 9.73 | 10.36 | ▁▁▁▁▇ |
| logdaysworked | 209 | 0.98 | 1.69 | 0.28 | 0.00 | 1.61 | 1.79 | 1.79 | 1.95 | ▁▁▁▁▇ |
| basewage | 12 | 1.00 | 1601.69 | 176.22 | 650.00 | 1500.00 | 1600.00 | 1700.00 | 2450.00 | ▁▁▇▂▁ |
| grosswage | 12 | 1.00 | 3095.24 | 988.18 | 1264.43 | 2453.00 | 2871.10 | 3539.66 | 14553.00 | ▇▂▁▁▁ |
| bonustotal | 12 | 1.00 | 1493.55 | 912.05 | 0.00 | 915.00 | 1284.43 | 1891.90 | 12853.00 | ▇▁▁▁▁ |
| promote_switch | 0 | 1.00 | 0.18 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| quitjob | 0 | 1.00 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| costofcommute | 0 | 1.00 | 7.28 | 7.24 | 0.00 | 2.00 | 6.00 | 10.00 | 55.00 | ▇▂▁▁▁ |
| age | 0 | 1.00 | 23.82 | 3.35 | 18.00 | 22.00 | 23.00 | 26.00 | 35.00 | ▅▇▅▁▁ |
| tenure | 0 | 1.00 | 21.74 | 18.78 | 2.00 | 8.00 | 15.00 | 31.00 | 94.00 | ▇▃▂▁▁ |
| commute | 0 | 1.00 | 103.78 | 65.85 | 10.00 | 50.00 | 80.00 | 165.00 | 300.00 | ▇▅▅▂▁ |
| married | 0 | 1.00 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
head(final_all)
## # A tibble: 6 × 32
## personid age tenure commute gender married higher_edu_indicator
## <dbl> <dbl> <dbl> <dbl> <fct> <dbl> <fct>
## 1 4122 30 94 180 Female 0 No Degree
## 2 6278 32 77 170 Female 1 Has Degree
## 3 7720 25 70 180 Female 0 Has Degree
## 4 8834 22 66 60 Female 0 No Degree
## 5 8854 22 66 60 Female 0 No Degree
## 6 10098 28 63 180 Female 1 Has Degree
## # ℹ 25 more variables: bedroom_indicator <fct>, children_indicator <fct>,
## # promote_switch <dbl>, quitjob <dbl>, costofcommute <dbl>,
## # mean_overall_perf_z_score <dbl>, mean_phonecall_perf_z_score <dbl>,
## # total_calls <dbl>, mean_log_phone_calls <dbl>,
## # mean_log_calls_per_second <dbl>, mean_log_average_call_lenght <dbl>,
## # mean_log_calls_per_day_worked <dbl>, mean_log_days_worked <dbl>,
## # weeks_observed <int>, remote_weeks <int>, on_site_weeks <int>, …
head(final_panel_weekly)
## # A tibble: 6 × 31
## personid year week exhaustion negative positive perform1 phonecall
## <dbl> <dbl> <dbl> <int> <int> <int> <dbl> <dbl>
## 1 4122 2022 49 9 20 20 -0.694 -0.729
## 2 4122 2022 50 8 21 25 1.14 1.37
## 3 4122 2022 51 8 20 24 0.0687 0.0139
## 4 4122 2022 52 6 17 22 -0.0442 -0.0542
## 5 4122 2022 53 12 19 19 -1.66 -1.64
## 6 4122 2023 2 12 18 19 0.594 0.909
## # ℹ 23 more variables: phonecallraw <dbl>, WFH_due_building_issues <fct>,
## # logphonecall <dbl>, logcallpersec <dbl>, logcalllength <dbl>,
## # logcall_dayworked <dbl>, logdaysworked <dbl>, date <date>, month <date>,
## # basewage <dbl>, grosswage <dbl>, bonustotal <dbl>, promote_switch <dbl>,
## # quitjob <dbl>, costofcommute <dbl>, age <dbl>, tenure <dbl>, commute <dbl>,
## # gender <fct>, married <dbl>, higher_edu_indicator <fct>,
## # bedroom_indicator <fct>, children_indicator <fct>
Below you find a short descriptive summary followed by a complete R code chunk containing all EDA steps in the exact sequence used during the analysis. This section provides a concise overview of the data cleaning, transformation, and exploratory steps conducted prior to modeling.
dim(), glimpse(), summary(), and
skim(), and checked missing values via
colSums(is.na()).log_wage, log_bonus, log_tenure)
to address skewness and improve model suitability.ggpairs().Inspected the data structure** using dim(),
glimpse(), summary(), and
skim()
# Quick overview of structure, summary stats, and missing values
dim(final_all)
## [1] 135 32
glimpse(final_all)
## Rows: 135
## Columns: 32
## $ personid <dbl> 4122, 6278, 7720, 8834, 8854, 10098, 103…
## $ age <dbl> 30, 32, 25, 22, 22, 28, 27, 30, 22, 28, …
## $ tenure <dbl> 94, 77, 70, 66, 66, 63, 62, 56, 54, 51, …
## $ commute <dbl> 180, 170, 180, 60, 60, 180, 60, 60, 20, …
## $ gender <fct> Female, Female, Female, Female, Female, …
## $ married <dbl> 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1…
## $ higher_edu_indicator <fct> No Degree, Has Degree, Has Degree, No De…
## $ bedroom_indicator <fct> No Bedroom, Has Own Bedroom, Has Own Bed…
## $ children_indicator <fct> No Children, Has Child/Children, No Chil…
## $ promote_switch <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1…
## $ quitjob <dbl> 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0…
## $ costofcommute <dbl> 18.00000, 12.00000, 9.00000, 0.00000, 4.…
## $ mean_overall_perf_z_score <dbl> 0.30980061, -0.07127851, -0.55909314, 0.…
## $ mean_phonecall_perf_z_score <dbl> 0.3254365292, 0.0006961388, -0.614241400…
## $ total_calls <dbl> 43132, 20430, 20136, 45957, 49621, 22216…
## $ mean_log_phone_calls <dbl> 6.136062, 5.901210, 5.780755, 6.287985, …
## $ mean_log_calls_per_second <dbl> -5.205012, -5.133074, -5.344394, -5.1700…
## $ mean_log_average_call_lenght <dbl> 11.34107, 11.01579, 11.09415, 11.45802, …
## $ mean_log_calls_per_day_worked <dbl> 9.561425, 9.524700, 9.534999, 9.651024, …
## $ mean_log_days_worked <dbl> 1.779649, 1.424983, 1.505603, 1.756560, …
## $ weeks_observed <int> 86, 68, 69, 86, 82, 51, 86, 27, 86, 86, …
## $ remote_weeks <int> 38, 0, 0, 0, 34, 0, 37, 0, 37, 0, 1, 0, …
## $ on_site_weeks <int> 48, 68, 69, 86, 48, 51, 49, 27, 49, 86, …
## $ pct_remote_weeks <dbl> 0.44186047, 0.00000000, 0.00000000, 0.00…
## $ pct_on_site_weeks <dbl> 0.5581395, 1.0000000, 1.0000000, 1.00000…
## $ median_base_wage_cny <dbl> 1900, 1800, 1850, 1900, 1850, 2375, 1800…
## $ median_gross_wage_cny <dbl> 4152.450, 3110.000, 3121.695, 3849.655, …
## $ median_bonus_total_cny <dbl> 2277.510, 1389.000, 1413.315, 2089.880, …
## $ months_observed <int> 26, 20, 26, 26, 26, 12, 26, 20, 26, 26, …
## $ exhaustion <int> 14, NA, NA, 4, NA, NA, 3, NA, 11, NA, NA…
## $ negative <int> 22, NA, NA, 12, NA, NA, 15, NA, 14, NA, …
## $ positive <int> 20, NA, NA, 29, NA, NA, 25, NA, 15, NA, …
summary(final_all)
## personid age tenure commute gender
## Min. : 4122 Min. :18.00 Min. : 2.00 Min. : 10.0 Female:67
## 1st Qu.:25913 1st Qu.:22.00 1st Qu.: 8.00 1st Qu.: 50.0 Male :68
## Median :37292 Median :23.00 Median :13.00 Median : 90.0
## Mean :32717 Mean :23.84 Mean :21.41 Mean :105.5
## 3rd Qu.:40464 3rd Qu.:25.50 3rd Qu.:31.00 3rd Qu.:167.5
## Max. :45442 Max. :35.00 Max. :94.00 Max. :300.0
##
## married higher_edu_indicator bedroom_indicator
## Min. :0.0000 No Degree :79 No Bedroom : 3
## 1st Qu.:0.0000 Has Degree:56 Has Own Bedroom:132
## Median :0.0000
## Mean :0.2148
## 3rd Qu.:0.0000
## Max. :1.0000
##
## children_indicator promote_switch quitjob costofcommute
## No Children :115 Min. :0.000 Min. :0.0000 Min. : 0.000
## Has Child/Children: 20 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.: 2.500
## Median :0.000 Median :0.0000 Median : 6.000
## Mean :0.163 Mean :0.2963 Mean : 7.401
## 3rd Qu.:0.000 3rd Qu.:1.0000 3rd Qu.:10.000
## Max. :1.000 Max. :1.0000 Max. :55.000
##
## mean_overall_perf_z_score mean_phonecall_perf_z_score total_calls
## Min. :-1.20014 Min. :-1.12002 Min. : 6088
## 1st Qu.:-0.40148 1st Qu.:-0.34469 1st Qu.:23760
## Median :-0.04058 Median :-0.03728 Median :33311
## Mean :-0.06438 Mean :-0.03998 Mean :31268
## 3rd Qu.: 0.28251 3rd Qu.: 0.27193 3rd Qu.:37984
## Max. : 1.41558 Max. : 1.22426 Max. :49621
##
## mean_log_phone_calls mean_log_calls_per_second mean_log_average_call_lenght
## Min. :5.311 Min. :-5.563 Min. :10.45
## 1st Qu.:5.890 1st Qu.:-5.243 1st Qu.:11.06
## Median :6.014 Median :-5.171 Median :11.17
## Mean :5.997 Mean :-5.172 Mean :11.16
## 3rd Qu.:6.133 3rd Qu.:-5.104 3rd Qu.:11.29
## Max. :6.588 Max. :-4.811 Max. :11.67
## NA's :1 NA's :1
## mean_log_calls_per_day_worked mean_log_days_worked weeks_observed
## Min. :8.673 Min. :1.342 Min. :27.00
## 1st Qu.:9.383 1st Qu.:1.616 1st Qu.:62.50
## Median :9.475 Median :1.665 Median :81.00
## Mean :9.466 Mean :1.677 Mean :73.11
## 3rd Qu.:9.603 3rd Qu.:1.759 3rd Qu.:85.00
## Max. :9.909 Max. :1.853 Max. :86.00
## NA's :1
## remote_weeks on_site_weeks pct_remote_weeks pct_on_site_weeks
## Min. : 0.00 Min. :26.00 Min. :0.0000 Min. :0.4194
## 1st Qu.: 0.00 1st Qu.:47.00 1st Qu.:0.0000 1st Qu.:0.5912
## Median : 1.00 Median :55.00 Median :0.0119 Median :0.9881
## Mean :14.36 Mean :58.76 Mean :0.1837 Mean :0.8163
## 3rd Qu.:33.00 3rd Qu.:72.00 3rd Qu.:0.4088 3rd Qu.:1.0000
## Max. :39.00 Max. :86.00 Max. :0.5806 Max. :1.0000
##
## median_base_wage_cny median_gross_wage_cny median_bonus_total_cny
## Min. :1300 Min. :1920 Min. : 517.8
## 1st Qu.:1550 1st Qu.:2518 1st Qu.: 972.0
## Median :1600 Median :2792 Median :1208.6
## Mean :1636 Mean :3024 Mean :1400.5
## 3rd Qu.:1700 3rd Qu.:3429 3rd Qu.:1759.7
## Max. :2375 Max. :4802 Max. :3052.0
##
## months_observed exhaustion negative positive
## Min. : 7.00 Min. : 0.00 Min. : 8.00 Min. :13.00
## 1st Qu.:19.50 1st Qu.: 3.00 1st Qu.:12.00 1st Qu.:21.00
## Median :26.00 Median : 9.00 Median :17.00 Median :24.00
## Mean :22.27 Mean : 8.59 Mean :16.64 Mean :24.25
## 3rd Qu.:26.00 3rd Qu.:12.00 3rd Qu.:20.00 3rd Qu.:27.00
## Max. :26.00 Max. :29.00 Max. :34.00 Max. :37.00
## NA's :74 NA's :74 NA's :74
skim(final_all)
| Name | final_all |
| Number of rows | 135 |
| Number of columns | 32 |
| _______________________ | |
| Column type frequency: | |
| factor | 4 |
| numeric | 28 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| gender | 0 | 1 | FALSE | 2 | Mal: 68, Fem: 67 |
| higher_edu_indicator | 0 | 1 | FALSE | 2 | No : 79, Has: 56 |
| bedroom_indicator | 0 | 1 | FALSE | 2 | Has: 132, No : 3 |
| children_indicator | 0 | 1 | FALSE | 2 | No : 115, Has: 20 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| personid | 0 | 1.00 | 32716.79 | 10835.70 | 4122.00 | 25913.00 | 37292.00 | 40464.00 | 45442.00 | ▁▂▂▃▇ |
| age | 0 | 1.00 | 23.84 | 3.35 | 18.00 | 22.00 | 23.00 | 25.50 | 35.00 | ▃▇▅▁▁ |
| tenure | 0 | 1.00 | 21.41 | 19.05 | 2.00 | 8.00 | 13.00 | 31.00 | 94.00 | ▇▃▂▁▁ |
| commute | 0 | 1.00 | 105.48 | 66.90 | 10.00 | 50.00 | 90.00 | 167.50 | 300.00 | ▇▅▅▂▁ |
| married | 0 | 1.00 | 0.21 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| promote_switch | 0 | 1.00 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| quitjob | 0 | 1.00 | 0.30 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| costofcommute | 0 | 1.00 | 7.40 | 7.22 | 0.00 | 2.50 | 6.00 | 10.00 | 55.00 | ▇▂▁▁▁ |
| mean_overall_perf_z_score | 0 | 1.00 | -0.06 | 0.54 | -1.20 | -0.40 | -0.04 | 0.28 | 1.42 | ▃▆▇▅▁ |
| mean_phonecall_perf_z_score | 0 | 1.00 | -0.04 | 0.49 | -1.12 | -0.34 | -0.04 | 0.27 | 1.22 | ▂▅▇▃▁ |
| total_calls | 0 | 1.00 | 31268.23 | 9635.28 | 6088.00 | 23760.00 | 33311.00 | 37984.50 | 49621.00 | ▁▃▅▇▃ |
| mean_log_phone_calls | 0 | 1.00 | 6.00 | 0.20 | 5.31 | 5.89 | 6.01 | 6.13 | 6.59 | ▁▃▇▆▁ |
| mean_log_calls_per_second | 1 | 0.99 | -5.17 | 0.12 | -5.56 | -5.24 | -5.17 | -5.10 | -4.81 | ▁▃▇▃▁ |
| mean_log_average_call_lenght | 1 | 0.99 | 11.16 | 0.20 | 10.45 | 11.06 | 11.17 | 11.29 | 11.67 | ▁▂▇▇▁ |
| mean_log_calls_per_day_worked | 1 | 0.99 | 9.47 | 0.20 | 8.67 | 9.38 | 9.48 | 9.60 | 9.91 | ▁▁▃▇▂ |
| mean_log_days_worked | 0 | 1.00 | 1.68 | 0.10 | 1.34 | 1.62 | 1.67 | 1.76 | 1.85 | ▁▂▇▆▇ |
| weeks_observed | 0 | 1.00 | 73.11 | 14.07 | 27.00 | 62.50 | 81.00 | 85.00 | 86.00 | ▁▁▂▃▇ |
| remote_weeks | 0 | 1.00 | 14.36 | 16.37 | 0.00 | 0.00 | 1.00 | 33.00 | 39.00 | ▇▁▁▂▅ |
| on_site_weeks | 0 | 1.00 | 58.76 | 16.79 | 26.00 | 47.00 | 55.00 | 72.00 | 86.00 | ▂▇▅▂▅ |
| pct_remote_weeks | 0 | 1.00 | 0.18 | 0.21 | 0.00 | 0.00 | 0.01 | 0.41 | 0.58 | ▇▁▁▅▁ |
| pct_on_site_weeks | 0 | 1.00 | 0.82 | 0.21 | 0.42 | 0.59 | 0.99 | 1.00 | 1.00 | ▁▅▁▁▇ |
| median_base_wage_cny | 0 | 1.00 | 1635.56 | 130.06 | 1300.00 | 1550.00 | 1600.00 | 1700.00 | 2375.00 | ▃▇▃▁▁ |
| median_gross_wage_cny | 0 | 1.00 | 3023.55 | 688.64 | 1919.76 | 2518.40 | 2792.00 | 3428.90 | 4801.74 | ▆▇▅▃▂ |
| median_bonus_total_cny | 0 | 1.00 | 1400.48 | 590.61 | 517.76 | 972.00 | 1208.60 | 1759.67 | 3051.98 | ▇▇▅▃▂ |
| months_observed | 0 | 1.00 | 22.27 | 5.05 | 7.00 | 19.50 | 26.00 | 26.00 | 26.00 | ▁▂▂▂▇ |
| exhaustion | 74 | 0.45 | 8.59 | 6.53 | 0.00 | 3.00 | 9.00 | 12.00 | 29.00 | ▇▇▅▁▁ |
| negative | 74 | 0.45 | 16.64 | 5.39 | 8.00 | 12.00 | 17.00 | 20.00 | 34.00 | ▇▆▇▁▁ |
| positive | 74 | 0.45 | 24.25 | 5.14 | 13.00 | 21.00 | 24.00 | 27.00 | 37.00 | ▂▆▇▅▁ |
colSums(is.na(final_all))
## personid age
## 0 0
## tenure commute
## 0 0
## gender married
## 0 0
## higher_edu_indicator bedroom_indicator
## 0 0
## children_indicator promote_switch
## 0 0
## quitjob costofcommute
## 0 0
## mean_overall_perf_z_score mean_phonecall_perf_z_score
## 0 0
## total_calls mean_log_phone_calls
## 0 0
## mean_log_calls_per_second mean_log_average_call_lenght
## 1 1
## mean_log_calls_per_day_worked mean_log_days_worked
## 1 0
## weeks_observed remote_weeks
## 0 0
## on_site_weeks pct_remote_weeks
## 0 0
## pct_on_site_weeks median_base_wage_cny
## 0 0
## median_gross_wage_cny median_bonus_total_cny
## 0 0
## months_observed exhaustion
## 0 74
## negative positive
## 74 74
This section visualizes the distributions of key numeric predictors —
including perf, age,
tenure_months, wage, bonus, and
commute_time_mins — using faceted histograms to identify
skewness, outliers, and the overall data spread.
# Histograms of numeric variables
final_all %>%
transmute(
perf = mean_overall_perf_z_score,
age,
tenure,
wage = median_gross_wage_cny,
bonus = median_bonus_total_cny,
commute,
costofcommute
) %>%
pivot_longer(
cols = everything(),
names_to = "var",
values_to = "value"
) %>%
ggplot(aes(x = value)) +
geom_histogram(bins = 30, fill = "#4C78A8", color = "black") +
facet_wrap(~ var, scales = "free") +
theme_bw() +
labs(
title = "Numeric distributions",
x = "Value",
y = "Count"
)
The variables bonus, wage, and tenure exhibit pronounced positive skewness, with most observations concentrated in the lower range and a few extreme high values pulling the mean upward. Due to this strong skewness, these variables were log-transformed to reduce the influence of outliers, stabilize variance, and approximate a more symmetric distribution suitable for statistical modeling.
The remaining variables show weaker distortions: age reflects a predominantly young workforce with slight right-skewness, perf is roughly symmetric around the mean, and commute_time_mins displays a broad, multimodal pattern indicating heterogeneous commuting distances.
# Create log-transformed versions of wage, bonus, and tenure
final_all <- final_all %>%
mutate(
# Lohn (brutto)
log_wage = if_else(
median_gross_wage_cny > 0,
log(median_gross_wage_cny),
NA_real_
),
# Bonus (0 möglich, daher +1)
log_bonus = if_else(
median_bonus_total_cny >= 0,
log(median_bonus_total_cny + 1),
NA_real_
),
# Tenure in Monaten/Jahren (wie in deinem Datensatz)
log_tenure = if_else(
tenure > 0,
log(tenure),
NA_real_
)
)
# Function to plot original vs. log-transformed distributions
plot_log_compare <- function(df, var, logvar) {
p1 <- ggplot(df, aes(x = .data[[var]])) +
geom_histogram(bins = 30, fill = "#4C78A8", color = "black") +
theme_bw() +
labs(title = paste(var, "original"), x = NULL, y = "Count")
p2 <- ggplot(df, aes(x = .data[[logvar]])) +
geom_histogram(bins = 30, fill = "#59A14F", color = "black") +
theme_bw() +
labs(title = paste(logvar, "log"), x = NULL, y = "Count")
gridExtra::grid.arrange(p1, p2, ncol = 2)
}
plot_log_compare(final_all, "median_gross_wage_cny", "log_wage")
plot_log_compare(final_all, "median_bonus_total_cny", "log_bonus")
plot_log_compare(final_all, "tenure", "log_tenure")
The visual comparison between the original and log-transformed
distributions confirms that the logarithmic transformation substantially
improves the statistical properties of bonus,
wage, and tenure. In the original histograms,
all three variables display heavy right tails, dense clustering at lower
values, and a small number of large observations that distort the
overall distribution. After applying the log transformation, these
variables become considerably more symmetric, with reduced tail length
and a more even spread of observations across the range. This
transformation therefore mitigates the influence of extreme values,
brings the distributions closer to normality, and enhances the
suitability of these variables for linear modeling and inferential
procedures that rely on homoscedasticity and approximate normality.
GGally::ggpairs(
final_all %>%
transmute(
age,
wage = median_gross_wage_cny,
bonus = median_bonus_total_cny,
tenure,
log_tenure,
commute,
costofcommute,
perf = mean_overall_perf_z_score
)
)
Displays frequency tables with percentages for key categorical
variables in final_all, including gender,
marital_status, children_indicator, and
higher_edu_indicator.
final_all %>% tabyl(gender) %>% adorn_pct_formatting()
## gender n percent
## Female 67 49.6%
## Male 68 50.4%
final_all %>% tabyl(married) %>% adorn_pct_formatting()
## married n percent
## 0 106 78.5%
## 1 29 21.5%
final_all %>% tabyl(children_indicator) %>% adorn_pct_formatting()
## children_indicator n percent
## No Children 115 85.2%
## Has Child/Children 20 14.8%
final_all %>% tabyl(higher_edu_indicator) %>% adorn_pct_formatting()
## higher_edu_indicator n percent
## No Degree 79 58.5%
## Has Degree 56 41.5%
Creates temporary grouping variables (tenure_group,
wage_group) within final_all and visualizes
categorical counts across gender,
marital_status, children_indicator, tenure
groups, and wage groups using faceted bar plots.
#Add temporary groups inside the pipe for plotting
final_all %>%
mutate(
tenure_group = cut(
tenure,
breaks = c(-Inf, 12, 36, Inf),
labels = c("<1y", "1–3y", ">3y")
),
wage_group = cut(
median_gross_wage_cny,
breaks = quantile(
median_gross_wage_cny,
probs = c(0, .33, .66, 1),
na.rm = TRUE
),
include.lowest = TRUE,
labels = c("Low", "Mid", "High")
)
) %>%
dplyr::select(gender, married, children_indicator,
tenure_group, wage_group) %>%
mutate(across(everything(), as.character)) %>% # ← diese Zeile neu
pivot_longer(
cols = everything(),
names_to = "category",
values_to = "group"
) %>%
ggplot(aes(x = group)) +
geom_bar(fill = "#4C78A8", color = "black") +
facet_wrap(~ category, scales = "free_x") +
theme_bw() +
labs(
title = "Categorical counts",
x = "Group",
y = "Count"
)
Now we create a binary promotion indicator and grouped
predictors, and examine how promotion rates differ across
demographic and job-related categories.
## Promotion by categories (wirklich nach Promotion unterschieden)
final_all %>%
mutate(
promotion_bin = if_else(
promote_switch == 1, "Promoted", "Not promoted"
),
tenure_group = cut(
tenure,
breaks = c(-Inf, 12, 36, Inf),
labels = c("<1y", "1–3y", ">3y")
),
wage_group = cut(
median_gross_wage_cny,
breaks = quantile(
median_gross_wage_cny,
probs = c(0, .33, .66, 1),
na.rm = TRUE
),
include.lowest = TRUE,
labels = c("Low", "Mid", "High")
)
) %>%
dplyr::select(promotion_bin, gender, married,
children_indicator, tenure_group, wage_group) %>%
mutate(across(-promotion_bin, as.character)) %>%
pivot_longer(
cols = -promotion_bin,
names_to = "category",
values_to = "group"
) %>%
ggplot(aes(x = group, fill = promotion_bin)) +
geom_bar(position = "fill") +
facet_wrap(~ category, scales = "free_x") +
theme_bw() +
labs(
title = "Promotion share by category",
x = "Group",
y = "Share promoted"
)
Compares numeric variables with promotion outcomes by creating
promotion_bin and visualizing group differences using
boxplots across key predictors.
#Numeric vs promotion (boxplots)
final_all %>%
mutate(
promotion_bin = if_else(
promote_switch == 1,
"Promoted",
"Not promoted"
),
promotion_bin = factor(promotion_bin,
levels = c("Not promoted", "Promoted"))
) %>%
transmute(
promotion_bin,
age,
wage = median_gross_wage_cny,
bonus = median_bonus_total_cny,
tenure,
log_tenure,
commute,
costofcommute,
perf = mean_overall_perf_z_score
) %>%
pivot_longer(
cols = -promotion_bin,
names_to = "var",
values_to = "value"
) %>%
ggplot(aes(x = promotion_bin, y = value, fill = promotion_bin)) +
geom_boxplot() +
scale_fill_manual(
values = c("Not promoted" = "#4C78A8",
"Promoted" = "#F58518"),
name = "Promotion"
) +
facet_wrap(~ var, scales = "free_y") +
theme_bw() +
labs(
title = "Numeric vs promotion",
x = "1",
y = "0"
)
#Numeric vs performance
final_all %>%
transmute(
perf = mean_overall_perf_z_score,
age,
tenure,
wage = median_gross_wage_cny,
bonus = median_bonus_total_cny,
commute,
costofcommute,
log_tenure
) %>%
pivot_longer(
cols = -perf,
names_to = "var",
values_to = "value"
) %>%
ggplot(aes(x = value, y = perf)) +
geom_point(alpha = 0.3, color = "#4C78A8") +
geom_smooth(method = "lm", se = FALSE, color = "#E45756") +
facet_wrap(~ var, scales = "free_x") +
theme_bw() +
labs(
title = "Performance vs numeric",
x = "Value",
y = "Performance (z)"
)
## `geom_smooth()` using formula = 'y ~ x'
#Performance by categories
final_all %>%
mutate(
tenure_group = cut(
tenure,
breaks = c(-Inf, 12, 36, Inf),
labels = c("<1y", "1–3y", ">3y")
),
wage_group = cut(
median_gross_wage_cny,
breaks = quantile(
median_gross_wage_cny,
probs = c(0, .33, .66, 1),
na.rm = TRUE
),
include.lowest = TRUE,
labels = c("Low", "Mid", "High")
)
) %>%
transmute(
perf = mean_overall_perf_z_score,
gender,
married,
children_indicator,
tenure_group,
wage_group
) %>%
mutate(across(-perf, as.character)) %>% # ← alle Gruppen-Variablen auf gleichen Typ
pivot_longer(
cols = -perf,
names_to = "category",
values_to = "group"
) %>%
ggplot(aes(x = group, y = perf, fill = group)) +
geom_boxplot() +
scale_fill_brewer(palette = "Set3") +
facet_wrap(~ category, scales = "free_x") +
theme_bw() +
labs(
title = "Performance by category",
x = "Group",
y = "Perf (z)"
) +
guides(fill = "none")
#Quit by categories
final_all %>%
mutate(
# quitjob ist 0/1 → 1 = Quit, 0 = Stayed (anpassbar falls anders)
quit_bin = if_else(quitjob == 1, "Quit", "Stayed"),
tenure_group = cut(
tenure,
breaks = c(-Inf, 12, 36, Inf),
labels = c("<1y", "1–3y", ">3y")
),
wage_group = cut(
median_gross_wage_cny,
breaks = quantile(
median_gross_wage_cny,
probs = c(0, .33, .66, 1),
na.rm = TRUE
),
include.lowest = TRUE,
labels = c("Low", "Mid", "High")
)
) %>%
dplyr::select(quit_bin, gender, married,
children_indicator, tenure_group, wage_group) %>%
mutate(across(-quit_bin, as.character)) %>% # Typen angleichen für pivot_longer
pivot_longer(
cols = -quit_bin,
names_to = "category",
values_to = "group"
) %>%
ggplot(aes(x = group, fill = quit_bin)) +
geom_bar(position = "fill") +
scale_y_continuous(labels = percent_format()) +
scale_fill_manual(
values = c("Stayed" = "#4DAF4A",
"Quit" = "#E41A1C"),
name = "Quit"
) +
facet_wrap(~ category, scales = "free_x") +
theme_bw() +
labs(
title = "Quit by category",
x = "Group",
y = "Share"
)
#Numeric vs quit (boxplots)
final_all %>%
mutate(
# quitjob ist 0/1 → 1 = Quit, 0 = Stayed
quit_bin = if_else(quitjob == 1, "Quit", "Stayed"),
quit_bin = factor(quit_bin, levels = c("Stayed", "Quit"))
) %>%
transmute(
quit_bin,
age,
wage = median_gross_wage_cny,
bonus = median_bonus_total_cny,
tenure, # statt tenure_in_months
log_tenure,
commute, # statt commute_time_mins
costofcommute,
perf = mean_overall_perf_z_score
) %>%
pivot_longer(
cols = -quit_bin,
names_to = "var",
values_to = "value"
) %>%
ggplot(aes(x = quit_bin, y = value, fill = quit_bin)) +
geom_boxplot() +
scale_fill_manual(
values = c("Stayed" = "#4DAF4A",
"Quit" = "#E41A1C"),
name = "Quit"
) +
facet_wrap(~ var, scales = "free_y") +
theme_bw() +
labs(
title = "Numeric vs quit",
x = "Quit status",
y = "Value"
)
rm(list = setdiff(ls(), c("final_all", "final_panel_weekly")))
After completing the exploratory data analysis and gaining an initial understanding of variable distributions, relationships, and group differences, the next step is to estimate statistical models that formally quantify these associations. Depending on the type of dependent variable, different regression methods are appropriate:
Choose the appropriate regression method:
– Linear (OLS) for continuous outcomes such as
’mean_overall_perf_z_score’
– LPM, Logit, or
Probit for binary outcomes such as ’promote_switch’ or
’quit_job’
The following section presents the regression specifications and the corresponding R code used in the analysis.
Below you find a short descriptive summary followed by the corresponding regression models and R code, presented in the exact sequence used during the analysis. This section provides a structured overview of the model-building process and outlines the key steps taken before selecting the preferred specifications.
# Checking collinearity among numerical independent variables (brief overview)
num_vars <- final_all |>
dplyr::select(mean_overall_perf_z_score, age, tenure, commute, costofcommute,
weeks_observed, remote_weeks, on_site_weeks,
pct_remote_weeks, pct_on_site_weeks,
total_calls, mean_log_phone_calls,
median_gross_wage_cny, median_bonus_total_cny,mean_log_days_worked)
# Correlation Matrix
num_vars %>%
correlate(use = "pairwise.complete.obs") %>%
fashion()
## Correlation computed with
## • Method: 'pearson'
## • Missing treated using: 'pairwise.complete.obs'
## term mean_overall_perf_z_score age tenure commute
## 1 mean_overall_perf_z_score .14 .28 .10
## 2 age .14 .52 -.04
## 3 tenure .28 .52 -.02
## 4 commute .10 -.04 -.02
## 5 costofcommute .02 .12 .00 .50
## 6 weeks_observed .46 -.03 .10 -.13
## 7 remote_weeks .21 .16 .04 -.18
## 8 on_site_weeks .18 -.18 .04 .07
## 9 pct_remote_weeks .16 .16 .02 -.18
## 10 pct_on_site_weeks -.16 -.16 -.02 .18
## 11 total_calls .80 .02 .19 -.06
## 12 mean_log_phone_calls .86 .05 .21 .10
## 13 median_gross_wage_cny .37 .33 .49 -.21
## 14 median_bonus_total_cny .36 .28 .40 -.22
## 15 mean_log_days_worked .04 -.12 -.19 -.12
## costofcommute weeks_observed remote_weeks on_site_weeks pct_remote_weeks
## 1 .02 .46 .21 .18 .16
## 2 .12 -.03 .16 -.18 .16
## 3 .00 .10 .04 .04 .02
## 4 .50 -.13 -.18 .07 -.18
## 5 -.08 .02 -.09 .03
## 6 -.08 .40 .45 .32
## 7 .02 .40 -.64 .99
## 8 -.09 .45 -.64 -.69
## 9 .03 .32 .99 -.69
## 10 -.03 -.32 -.99 .69 -1.00
## 11 -.07 .85 .35 .37 .28
## 12 -.05 .39 .10 .23 .05
## 13 -.25 .42 .25 .10 .22
## 14 -.26 .43 .25 .11 .22
## 15 -.18 .44 .31 .07 .29
## pct_on_site_weeks total_calls mean_log_phone_calls median_gross_wage_cny
## 1 -.16 .80 .86 .37
## 2 -.16 .02 .05 .33
## 3 -.02 .19 .21 .49
## 4 .18 -.06 .10 -.21
## 5 -.03 -.07 -.05 -.25
## 6 -.32 .85 .39 .42
## 7 -.99 .35 .10 .25
## 8 .69 .37 .23 .10
## 9 -1.00 .28 .05 .22
## 10 -.28 -.05 -.22
## 11 -.28 .70 .48
## 12 -.05 .70 .33
## 13 -.22 .48 .33
## 14 -.22 .49 .33 .98
## 15 -.29 .42 .06 .39
## median_bonus_total_cny mean_log_days_worked
## 1 .36 .04
## 2 .28 -.12
## 3 .40 -.19
## 4 -.22 -.12
## 5 -.26 -.18
## 6 .43 .44
## 7 .25 .31
## 8 .11 .07
## 9 .22 .29
## 10 -.22 -.29
## 11 .49 .42
## 12 .33 .06
## 13 .98 .39
## 14 .45
## 15 .45
based on the correlations in our correlation table we start building or models first we do it by group
to iteratively add more variables and see how the model improves we first group our variables by different categories. After that the variables will be added step by step to see how the model improves. Of course it will be checked for multicollinearity.
- Demographics: age, gender, married, children, high_educ, months_observed, tenure, commute
- Work Arrangement: remote_weeks, pct_remote_weeks, on_site_weeks, pct_on_site_weeks
- Work Intensity: total_calls, mean_log_phone_calls, mean_log_calls_per_second, mean_log_average_call_lenght, mean_log_calls_per_day_worked, mean_log_days_worked
- Salary and Commute: promote_switch, quitjob, median_base_wage_cny, median_gross_wage_cny, median_bonus_total_cny, cost_of_commute_cny, commute_time_mins
- Well-being: exhaustion, negative, positive, bedroom_indicator
we will state beforehand that the well-being variables will be from no use for us (except bedroom_indicator) in this analysis as they reduce our sample size by half.
m0 <- lm(mean_overall_perf_z_score ~
pct_remote_weeks,
data = final_all)
#Work arrangement:
suppressWarnings(
stargazer(m0,
type = "text",
title = "Regression Results Model 0",
dep.var.labels = "Mean Overall Performance Z-Score",
covariate.labels = c("Remote Work Percentage"),
out = "regression_results_model_0.txt",
no.space = TRUE)
)
##
## Regression Results Model 0
## =======================================================
## Dependent variable:
## --------------------------------
## Mean Overall Performance Z-Score
## -------------------------------------------------------
## Remote Work Percentage 0.421*
## (0.225)
## Constant -0.142**
## (0.062)
## -------------------------------------------------------
## Observations 135
## R2 0.026
## Adjusted R2 0.018
## Residual Std. Error 0.535 (df = 133)
## F Statistic 3.512* (df = 1; 133)
## =======================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
# Breusch-Pagan test for heteroscedasticity
bptest(m0)
##
## studentized Breusch-Pagan test
##
## data: m0
## BP = 0.75419, df = 1, p-value = 0.3852
There is no heteroscedasticity present
We cannot check for multicollinearity with VIF here as there is only one independent variable.
With 10% CI the pct_remote_weeks is stat. significant. With an increase of 10 percentage points in remote work percentage, the mean overall performance z-score increases by 0.42 Very low R squared the model doesn’t explain much of the variability of the mean overall performance z-score
So let’s add a few demographic variables to see if we can improve the model
#m0 + Demographic & Job Characteristics:
m1 <- lm(mean_overall_perf_z_score ~
pct_remote_weeks +
gender +
log(tenure),
data = final_all)
suppressWarnings(
stargazer(m1,
type = "text",
title = "Regression Results Model 1",
dep.var.labels = "Mean Overall Performance Z-Score",
covariate.labels = c("remote work percentage",
"gender (male=1)",
"log(tenure)"),
out = "regression_results_model_1.txt",
no.space = TRUE)
)
##
## Regression Results Model 1
## =======================================================
## Dependent variable:
## --------------------------------
## Mean Overall Performance Z-Score
## -------------------------------------------------------
## remote work percentage 0.474**
## (0.195)
## gender (male=1) -0.362***
## (0.083)
## log(tenure) 0.158***
## (0.041)
## Constant -0.385***
## (0.136)
## -------------------------------------------------------
## Observations 135
## R2 0.277
## Adjusted R2 0.261
## Residual Std. Error 0.464 (df = 131)
## F Statistic 16.752*** (df = 3; 131)
## =======================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
Assumption-check:
# Breusch-Pagan test for heteroscedasticity
bptest(m1)
##
## studentized Breusch-Pagan test
##
## data: m1
## BP = 6.398, df = 3, p-value = 0.09377
There is no heteroscedasticity present
# Variance Inflation Factor for multicollinearity
vif(m1)
## pct_remote_weeks gender log(tenure)
## 1.002815 1.075554 1.072640
There is no multicollinearity present.
By adding the variables gender and log(tenure) the remote work percentage becomes statistically significant at a 5% level. An increase of 10 percentage points in remote work percentage is associated with an increase of 0.474 in the mean overall performance z-score, keeping everything else constant. We also conclude that by adding gender to the regression, being a man decreases the mean overall performance z-score by 0.362 compared to being a woman, with a confidence of 99%, ceteris paribus. Finally, we see that an increase in tenure of 1% would increase by 0.00158 the mean overall performance z-score, ceteris paribus. The R2 and adjusted R2 also increases considerably from model 0 to model 1, indicating a better fit of the model. Model 1 justifies around 26.1% of the variability of the mean overall performance z-score. F statistic is also stat. significant at 1% level, indicating that at least one of the independent variables is stat. significant in explaining the variability of the dependent variable.
#m0 + Demographic + (tenure without logs)
m1_1 <- lm(mean_overall_perf_z_score ~
pct_remote_weeks +
gender +
tenure,
data= final_all)
suppressWarnings(
stargazer(m1_1,
type = "text",
title = "Regression Results Model 1 (Tenure without log)",
dep.var.labels = "Mean Overall Performance Z-Score",
covariate.labels = c("remote work percentage",
"gender (male=1)",
"tenure"),
out = "regression_results_model_1_without_log.txt",
no.space = TRUE)
)
##
## Regression Results Model 1 (Tenure without log)
## =======================================================
## Dependent variable:
## --------------------------------
## Mean Overall Performance Z-Score
## -------------------------------------------------------
## remote work percentage 0.463**
## (0.202)
## gender (male=1) -0.394***
## (0.086)
## tenure 0.005**
## (0.002)
## Constant -0.060
## (0.090)
## -------------------------------------------------------
## Observations 135
## R2 0.226
## Adjusted R2 0.209
## Residual Std. Error 0.480 (df = 131)
## F Statistic 12.776*** (df = 3; 131)
## =======================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
Assumption-check:
# Breusch-Pagan test for heteroscedasticity
bptest(m1_1)
##
## studentized Breusch-Pagan test
##
## data: m1_1
## BP = 5.9013, df = 3, p-value = 0.1165
There is no heteroscedasticity present
# Variance Inflation Factor for multicollinearity
vif(m1_1)
## pct_remote_weeks gender tenure
## 1.003905 1.073801 1.071207
There is no multicollinearity present.
We did this regressions without logs on the independent variable tenure because level-log models are extremely rare in the literature as well as the beta1 was higher for the regression for the model with tenure in levels. However, when we replace log(tenure) with tenure in levels, the coefficient becomes numerically larger (0.005 vs 0.0016), but this comparison is misleading because the units differ. In the level–log model, 0.0016 is the effect of a 1% increase in tenure, whereas 0.005 is the effect of a 1-unit increase in tenure. The model with log(tenure) has a higher adjusted R² and lower residual standard error, and it captures diminishing returns to tenure, which is theoretically reasonable. Therefore, we prefer the specification with log(tenure).
#m1 + Wage and well being commute:
m2 <- lm(mean_overall_perf_z_score ~
pct_remote_weeks +
gender +
log(tenure) +
bedroom_indicator +
promote_switch,
data = final_all)
suppressWarnings(
stargazer(m2,
type = "text",
title = "Regression Results Model 2",
dep.var.labels = "Mean Overall Performance Z-Score",
covariate.labels = c("remote work percentage",
"gender (male=1)",
"log(tenure)",
"bedroom (has own=1)",
"promotion status (promoted=1)"),
out = "regression_results_model_2.txt",
no.space = TRUE)
)
##
## Regression Results Model 2
## ==============================================================
## Dependent variable:
## --------------------------------
## Mean Overall Performance Z-Score
## --------------------------------------------------------------
## remote work percentage 0.495**
## (0.193)
## gender (male=1) -0.382***
## (0.082)
## log(tenure) 0.154***
## (0.042)
## bedroom (has own=1) 0.332
## (0.275)
## promotion status (promoted=1) 0.226**
## (0.109)
## Constant -0.729**
## (0.323)
## --------------------------------------------------------------
## Observations 135
## R2 0.311
## Adjusted R2 0.284
## Residual Std. Error 0.457 (df = 129)
## F Statistic 11.654*** (df = 5; 129)
## ==============================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
Assumption-check:
# Breusch-Pagan test for heteroscedasticity
bptest(m2)
##
## studentized Breusch-Pagan test
##
## data: m2
## BP = 7.6693, df = 5, p-value = 0.1754
There is no heteroscedasticity present.
# Variance Inflation Factor for multicollinearity
vif(m2)
## pct_remote_weeks gender log(tenure) bedroom_indicator
## 1.006445 1.091508 1.162332 1.063217
## promote_switch
## 1.046492
There is no multicollinearity present.
The adj. R2 of the model increased from 0.261 to 0.284, indicating a better fit of the model, due to promotion status being stat. significant and being added to the model. F statistic is also stat. significant at 1% level, indicating that at least one of the independent variables is stat. significant in explaining the variability of the dependent variable.
#m2 + Well being + Wage and commute:
m3_1 <- lm(mean_overall_perf_z_score ~
pct_remote_weeks +
gender +
log_tenure +
bedroom_indicator +
promote_switch +
commute +
log(median_gross_wage_cny),
data = final_all)
summary(m3_1)
##
## Call:
## lm(formula = mean_overall_perf_z_score ~ pct_remote_weeks + gender +
## log_tenure + bedroom_indicator + promote_switch + commute +
## log(median_gross_wage_cny), data = final_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.04784 -0.25847 0.02245 0.26450 1.02552
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.0314840 1.6416599 -3.674 0.000351 ***
## pct_remote_weeks 0.4127115 0.1925680 2.143 0.034004 *
## genderMale -0.3752290 0.0798992 -4.696 6.77e-06 ***
## log_tenure 0.1085336 0.0446083 2.433 0.016365 *
## bedroom_indicatorHas Own Bedroom 0.4176988 0.2647864 1.577 0.117170
## promote_switch 0.1820333 0.1056556 1.723 0.087342 .
## commute 0.0012901 0.0005909 2.183 0.030854 *
## log(median_gross_wage_cny) 0.6534883 0.2046973 3.192 0.001778 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4374 on 127 degrees of freedom
## Multiple R-squared: 0.3775, Adjusted R-squared: 0.3432
## F-statistic: 11 on 7 and 127 DF, p-value: 8.178e-11
suppressWarnings(
stargazer(m3_1,
type = "text",
title = "Regression Results Model 3",
dep.var.labels = "Mean Overall Performance Z-Score",
covariate.labels = c("remote work percentage",
"gender (male=1)",
"log(tenure)",
"bedroom (has own=1)",
"promotion status (promoted=1)",
"commute time (mins)",
"log median gross wage (CNY)"),
out = "regression_results_model_3.txt",
no.space = TRUE)
)
##
## Regression Results Model 3
## ==============================================================
## Dependent variable:
## --------------------------------
## Mean Overall Performance Z-Score
## --------------------------------------------------------------
## remote work percentage 0.413**
## (0.193)
## gender (male=1) -0.375***
## (0.080)
## log(tenure) 0.109**
## (0.045)
## bedroom (has own=1) 0.418
## (0.265)
## promotion status (promoted=1) 0.182*
## (0.106)
## commute time (mins) 0.001**
## (0.001)
## log median gross wage (CNY) 0.653***
## (0.205)
## Constant -6.031***
## (1.642)
## --------------------------------------------------------------
## Observations 135
## R2 0.378
## Adjusted R2 0.343
## Residual Std. Error 0.437 (df = 127)
## F Statistic 11.003*** (df = 7; 127)
## ==============================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
Assumption-check:
# Breusch-Pagan test for heteroscedasticity
bptest(m3_1)
##
## studentized Breusch-Pagan test
##
## data: m3_1
## BP = 8.6135, df = 7, p-value = 0.2816
There is no heteroscedasticity present
# Variance Inflation Factor for multicollinearity
vif(m3_1)
## pct_remote_weeks gender
## 1.097155 1.126087
## log_tenure bedroom_indicator
## 1.408966 1.074955
## promote_switch commute
## 1.074459 1.094362
## log(median_gross_wage_cny)
## 1.394667
There is no multicollinearity present.
Regression with median gross wage logged instead of median bonus total wage logged, because of the higher correlation with performance score but we will see that bonus total wage gives a better fit of the model
#m2 + Well being + Wage and commute:
m3 <- lm(mean_overall_perf_z_score ~
pct_remote_weeks +
gender +
log(tenure) +
bedroom_indicator +
promote_switch +
commute +
median_bonus_total_cny,
data = final_all)
summary(m3)
##
## Call:
## lm(formula = mean_overall_perf_z_score ~ pct_remote_weeks + gender +
## log(tenure) + bedroom_indicator + promote_switch + commute +
## median_bonus_total_cny, data = final_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0478 -0.2509 0.0303 0.2762 1.0112
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.200e+00 3.319e-01 -3.616 0.000431 ***
## pct_remote_weeks 4.115e-01 1.904e-01 2.161 0.032575 *
## genderMale -3.727e-01 7.924e-02 -4.703 6.58e-06 ***
## log(tenure) 1.164e-01 4.295e-02 2.710 0.007659 **
## bedroom_indicatorHas Own Bedroom 4.249e-01 2.628e-01 1.617 0.108372
## promote_switch 1.800e-01 1.048e-01 1.719 0.088131 .
## commute 1.352e-03 5.880e-04 2.298 0.023170 *
## median_bonus_total_cny 2.536e-04 7.244e-05 3.500 0.000642 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4342 on 127 degrees of freedom
## Multiple R-squared: 0.3867, Adjusted R-squared: 0.3529
## F-statistic: 11.44 on 7 and 127 DF, p-value: 3.363e-11
suppressWarnings(
stargazer(m3,
type = "text",
title = "Regression Results Model 3",
dep.var.labels = "Mean Overall Performance Z-Score",
covariate.labels = c("remote work percentage",
"gender (male=1)",
"log(tenure)",
"bedroom (has own=1)",
"promotion status (promoted=1)",
"commute time (mins)",
"median bonus total (CNY)"),
out = "regression_results_model_3.txt",
no.space = TRUE)
)
##
## Regression Results Model 3
## ==============================================================
## Dependent variable:
## --------------------------------
## Mean Overall Performance Z-Score
## --------------------------------------------------------------
## remote work percentage 0.411**
## (0.190)
## gender (male=1) -0.373***
## (0.079)
## log(tenure) 0.116***
## (0.043)
## bedroom (has own=1) 0.425
## (0.263)
## promotion status (promoted=1) 0.180*
## (0.105)
## commute time (mins) 0.001**
## (0.001)
## median bonus total (CNY) 0.0003***
## (0.0001)
## Constant -1.200***
## (0.332)
## --------------------------------------------------------------
## Observations 135
## R2 0.387
## Adjusted R2 0.353
## Residual Std. Error 0.434 (df = 127)
## F Statistic 11.440*** (df = 7; 127)
## ==============================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
# bp-test for model3
bptest(m3)
##
## studentized Breusch-Pagan test
##
## data: m3
## BP = 8.9877, df = 7, p-value = 0.2535
There is no heteroscedasticity present
# vif for model3
vif(m3)
## pct_remote_weeks gender log(tenure)
## 1.088885 1.124158 1.325743
## bedroom_indicator promote_switch commute
## 1.074821 1.072122 1.100011
## median_bonus_total_cny
## 1.301186
There is no multicolinearity present
#m3 but with log of median bonus total
m3_2 <- lm(mean_overall_perf_z_score ~
pct_remote_weeks +
gender +
log(tenure) +
bedroom_indicator +
promote_switch +
commute +
log(median_bonus_total_cny),
data = final_all)
summary(m3_2)
##
## Call:
## lm(formula = mean_overall_perf_z_score ~ pct_remote_weeks + gender +
## log(tenure) + bedroom_indicator + promote_switch + commute +
## log(median_bonus_total_cny), data = final_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.06792 -0.25844 0.02354 0.25677 1.01911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.4547323 0.7853807 -4.399 2.28e-05 ***
## pct_remote_weeks 0.4130739 0.1898238 2.176 0.031399 *
## genderMale -0.3776625 0.0791752 -4.770 4.97e-06 ***
## log(tenure) 0.1205582 0.0424340 2.841 0.005239 **
## bedroom_indicatorHas Own Bedroom 0.4354327 0.2625981 1.658 0.099751 .
## promote_switch 0.1727385 0.1048452 1.648 0.101916
## commute 0.0013470 0.0005866 2.296 0.023296 *
## log(median_bonus_total_cny) 0.3621301 0.1014060 3.571 0.000503 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4334 on 127 degrees of freedom
## Multiple R-squared: 0.3889, Adjusted R-squared: 0.3552
## F-statistic: 11.55 on 7 and 127 DF, p-value: 2.714e-11
suppressWarnings(
stargazer(m3_2,
type = "text",
title = "Regression Results Model 3",
dep.var.labels = "Mean Overall Performance Z-Score",
covariate.labels = c("remote work percentage",
"gender (male=1)",
"log(tenure)",
"bedroom (has own=1)",
"promotion status (promoted=1)",
"commute time (mins)",
"log median bonus total (CNY)"))
)
##
## Regression Results Model 3
## ==============================================================
## Dependent variable:
## --------------------------------
## Mean Overall Performance Z-Score
## --------------------------------------------------------------
## remote work percentage 0.413**
## (0.190)
##
## gender (male=1) -0.378***
## (0.079)
##
## log(tenure) 0.121***
## (0.042)
##
## bedroom (has own=1) 0.435*
## (0.263)
##
## promotion status (promoted=1) 0.173
## (0.105)
##
## commute time (mins) 0.001**
## (0.001)
##
## log median bonus total (CNY) 0.362***
## (0.101)
##
## Constant -3.455***
## (0.785)
##
## --------------------------------------------------------------
## Observations 135
## R2 0.389
## Adjusted R2 0.355
## Residual Std. Error 0.433 (df = 127)
## F Statistic 11.547*** (df = 7; 127)
## ==============================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
By logging the skewed model of median bonus total wage we get a better fit of the model, because the bonus was right skewed. R2 increases from 0.387 to 0.389 and adj R2 from 0.353 to 0.355 The F statistic shows stat significance at 1% level, indicating that at least one of the independent variables is stat. significant in explaining the variability of the dependent variable. Bedroom becomes stat. insignificant at 10% level but promotion status loses significance in explaining the performance. With an increase of 1% in bonus total the performance increases by 0.00362. Before without the log, with an increase of 1 CNY the performance increased by 0.0003. By logging the bonus also has a bigger impact on performance.
As this is the best model we have so far, we will now do the interpretations for the coefficients and check the assumptions of linear regression for this model.
An increase of 1 percentage point in remote work increases the performance score by 0.413 with a confidence of 95%. Being male decreases the performance score by 0.378, with a significance of 99%. When tenure increases by 1 month performance increases by 0.00121 with a confidence interval of 99%. The employees that live in a house with one bedroom have a performance score that is 0.435 higher than those who do not have their own bedroom, with a confidence of 90%. Commute time is stat. significant at a 5% level, however the coefficient is very small, with an increase of 1 minute in commute time the performance score increases by 0.001. When the median bonus increases by 1 % the performance increases by 0.00362.
#BP test
bptest(m3_2)
##
## studentized Breusch-Pagan test
##
## data: m3_2
## BP = 10.356, df = 7, p-value = 0.1693
large p-value, we accept the null hypotheses so there is homoskedasticity and no heteroscedasticity present
#VIF
vif(m3_2)
## pct_remote_weeks gender
## 1.086008 1.126413
## log(tenure) bedroom_indicator
## 1.298760 1.076995
## promote_switch commute
## 1.077790 1.098669
## log(median_bonus_total_cny)
## 1.276829
all values close to 1 so no multicollinearity present
mutate() to generate clean binary variables for
promotion, gender, married,
higher_edu_indicator, and
children_indicator.summarise() to obtain n_obs,
promo_rate, avg_age, and
avg_perf.promotion, gender,
mean_overall_perf_z_score, age,
tenure_in_months, higher_edu_indicator,
married) using cor().gender, performance distributions by
promotion, and promotion differences by
married status using ggplot().performance,
married, age, tenure_in_months,
and higher_edu_indicator.promotion ~ gender + performance + age + tenure + higher_edu_indicator + married.OLS,
Logit, and Probit using logLik()
and AIC() to identify the preferred functional form.vif() for Logit and Probit
models.margins() to interpret effects in probability terms.pR2() as a goodness-of-fit metric for binary
outcome models.All key predictors and the promotion outcome are converted into binary indicators to enable consistent regression analysis.
final_all <- final_all %>%
mutate(
promotion = case_when(
promote_switch %in% c(1, "1") ~ 1L,
promote_switch %in% c(0, "0") ~ 0L,
TRUE ~ NA_integer_
)
)
# Check: gibt es wirklich 0 UND 1?
table(final_all$promotion, useNA = "ifany")
##
## 0 1
## 113 22
This code converts promote_switch into a clean binary
promotion variable (promotion), ensuring that both numeric
(0/1) and character
("0"/"1") values are handled correctly. The
table() check confirms that both categories (0
and 1) are present, which is essential because
Logit and Probit models require variation in
the dependent variable to be estimable.
#Deskriptive statistic
desc <- final_all %>%
summarise(
n_obs = n(),
promo_rate = mean(promotion, na.rm = TRUE),
avg_age = mean(age, na.rm = TRUE),
avg_perf = mean(mean_overall_perf_z_score, na.rm = TRUE)
)
desc
## # A tibble: 1 × 4
## n_obs promo_rate avg_age avg_perf
## <int> <dbl> <dbl> <dbl>
## 1 135 0.163 23.8 -0.0644
The descriptive summary provides a first overview of the dataset. We
observe a total of 135 employees (n_obs = 135). The overall
promotion rate is relatively low at around 16%
(promo_rate ≈ 0.163), indicating that promotions are
selective events in this sample. The average age of employees is about
23.8 years (avg_age), suggesting a relatively young
workforce. The mean performance score (avg_perf) is close
to zero, which is expected because the variable is standardized
(z-score). This descriptive overview confirms that the dependent
variable promotion has sufficient variation for subsequent
Logit and Probit models, and it provides
initial context for interpreting the regression results.
# Correlation matrix (numeric variables only)
# Select variables to include in the correlation matrix
vars <- c(
"promotion",
"mean_overall_perf_z_score",
"age",
"tenure",
"married",
"children_indicator",
"higher_edu_indicator"
)
# Convert all variables to numeric (important for 0/1 indicators)
corr_mat <- final_all %>%
dplyr::select(all_of(vars)) %>%
mutate(across(everything(), as.numeric)) %>%
cor(use = "pairwise.complete.obs")
# Display a clean, regular correlation matrix in the knitted output
knitr::kable(
corr_mat,
caption = "Correlation Matrix",
digits = 3
)
| promotion | mean_overall_perf_z_score | age | tenure | married | children_indicator | higher_edu_indicator | |
|---|---|---|---|---|---|---|---|
| promotion | 1.000 | 0.173 | -0.028 | 0.074 | -0.133 | -0.071 | -0.087 |
| mean_overall_perf_z_score | 0.173 | 1.000 | 0.137 | 0.277 | 0.177 | 0.172 | 0.013 |
| age | -0.028 | 0.137 | 1.000 | 0.521 | 0.506 | 0.551 | 0.314 |
| tenure | 0.074 | 0.277 | 0.521 | 1.000 | 0.352 | 0.361 | 0.183 |
| married | -0.133 | 0.177 | 0.506 | 0.352 | 1.000 | 0.797 | 0.255 |
| children_indicator | -0.071 | 0.172 | 0.551 | 0.361 | 0.797 | 1.000 | 0.157 |
| higher_edu_indicator | -0.087 | 0.013 | 0.314 | 0.183 | 0.255 | 0.157 | 1.000 |
The correlation matrix shows that promotion is only
weakly correlated with all other variables, indicating no strong
bivariate drivers of promotion. Performance has a small positive
correlation with promotion (≈ 0.17), consistent with expectations. Age
and tenure correlate strongly (≈ 0.52), and both are also moderately
related to marital and children status. The very high correlation
between married and children_indicator (≈
0.80) indicates substantial overlap, which is why only
married is included in the regression models to avoid
multicollinearity. Higher education shows only weak relationships with
all other variables.
# Probit-Modele M1–M5
mod1 <- glm(
promotion ~ gender,
data = final_all,
family = binomial(link = "probit")
)
mod2 <- glm(
promotion ~ gender + mean_overall_perf_z_score,
data = final_all,
family = binomial(link = "probit")
)
mod3 <- glm(
promotion ~ gender + mean_overall_perf_z_score + married,
data = final_all,
family = binomial(link = "probit")
)
mod4 <- glm(
promotion ~ gender + mean_overall_perf_z_score + married + tenure,
data = final_all,
family = binomial(link = "probit")
)
mod5 <- glm(
promotion ~ gender + mean_overall_perf_z_score + married +
tenure + higher_edu_indicator,
data = final_all,
family = binomial(link = "probit")
)
suppressWarnings(
stargazer(
mod1, mod2, mod3, mod4, mod5,
type = "text",
no.space = TRUE,
digits = 3,
column.labels = c("M1","M2","M3","M4","M5"),
dep.var.labels = "Promotion",
covariate.labels = c(
"Male",
"Perf (z-score)",
"Married",
"Tenure",
"Higher Edu"
),
omit.stat = "ser"
)
)
##
## ===================================================================
## Dependent variable:
## -------------------------------------------------
## Promotion
## M1 M2 M3 M4 M5
## (1) (2) (3) (4) (5)
## -------------------------------------------------------------------
## Male 0.233 0.538* 0.566* 0.633** 0.631**
## (0.260) (0.294) (0.305) (0.313) (0.312)
## Perf (z-score) 0.707** 0.849*** 0.793*** 0.790***
## (0.278) (0.297) (0.301) (0.302)
## Married -0.812* -0.988** -0.944**
## (0.419) (0.448) (0.454)
## Tenure 0.011 0.011
## (0.008) (0.008)
## Higher Edu -0.190
## (0.293)
## Constant -1.106*** -1.273*** -1.159*** -1.417*** -1.353***
## (0.193) (0.212) (0.224) (0.296) (0.306)
## -------------------------------------------------------------------
## Observations 135 135 135 135 135
## Log Likelihood -59.613 -56.208 -53.991 -53.045 -52.829
## Akaike Inf. Crit. 123.225 118.417 115.981 116.091 117.658
## ===================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
cat("\nModel specs:\n",
"M1 = Gender only\n",
"M2 = M1 + Performance\n",
"M3 = M2 + Married\n",
"M4 = M3 + Age\n",
"M5 = M4 + Tenure + Higher Edu\n")
##
## Model specs:
## M1 = Gender only
## M2 = M1 + Performance
## M3 = M2 + Married
## M4 = M3 + Age
## M5 = M4 + Tenure + Higher Edu
The stepwise Probit models (M1–M5) illustrate how omitted variable
bias (OVB) affects the estimated gender effect. In M1, Male
is positive but not significant, but once performance is added in M2,
the coefficient becomes significant and increases in magnitude. This
indicates that the simple bivariate model suffers from OVB because
performance is correlated with both gender and promotion. After
controlling for this, the underlying gender effect becomes visible.
Performance is consistently positive and highly significant across
all models, confirming it as the strongest driver of promotion. When
family-status variables are introduced, an important specification
decision arises: married and
children_indicator correlate very strongly (≈ 0.80). To
avoid multicollinearity, only one of them should be included in the
regression. Since married is the variable that becomes
statistically significant when added (M3), it is selected as the
preferred control, while children_indicator is omitted from
the main models.
In M3–M5, Married shows a negative and significant
association with promotion, suggesting lower promotion probabilities for
married employees. Meanwhile, tenure, and higher education do not become
significant once performance and demographic factors are controlled
for.
Overall, the sequence M1–M5 demonstrates clear evidence of OVB in the
gender coefficient, highlights performance as the dominant predictor,
and justifies the choice of married (instead of
children_indicator) due to both statistical significance
and the need to avoid multicollinearity.
#OLS vs Logit vs Probit (Comparison)
ols_full <- lm(
promotion ~ gender + mean_overall_perf_z_score +
age + tenure + higher_edu_indicator + married,
data = final_all
)
logit_full <- glm(
promotion ~ gender + mean_overall_perf_z_score +
age + tenure + higher_edu_indicator + married,
data = final_all,
family = binomial("logit")
)
probit_full <- glm(
promotion ~ gender + mean_overall_perf_z_score +
age + tenure + higher_edu_indicator + married,
data = final_all,
family = binomial("probit")
)
suppressWarnings(
stargazer(
ols_full, logit_full, probit_full,
type = "text",
no.space = TRUE,
digits = 3,
dep.var.labels = "Promotion",
covariate.labels = c(
"Male",
"Performance (Z)",
"Age",
"Tenure",
"Higher Edu",
"Married"
),
omit.stat = "ser"
)
)
##
## =========================================================
## Dependent variable:
## ---------------------------------------
## Promotion
## OLS logistic probit
## (1) (2) (3)
## ---------------------------------------------------------
## Male 0.142** 1.195** 0.677**
## (0.071) (0.583) (0.324)
## Performance (Z) 0.169** 1.436*** 0.803***
## (0.065) (0.553) (0.306)
## Age -0.004 -0.047 -0.030
## (0.013) (0.105) (0.059)
## Tenure 0.003 0.021 0.013
## (0.002) (0.015) (0.009)
## Higher Edu -0.045 -0.337 -0.162
## (0.067) (0.542) (0.300)
## Married -0.150* -1.558* -0.870*
## (0.090) (0.910) (0.476)
## Constant 0.199 -1.308 -0.749
## (0.266) (2.220) (1.244)
## ---------------------------------------------------------
## Observations 135 135 135
## R2 0.097
## Adjusted R2 0.054
## Log Likelihood -52.881 -52.701
## Akaike Inf. Crit. 119.762 119.402
## F Statistic 2.281** (df = 6; 128)
## =========================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
We estimate OLS, logit, and probit models
to ensure that our findings are not driven by a specific estimation
method. Since promotion is a binary outcome, logit and
probit are the theoretically appropriate nonlinear models,
while OLS provides a simple baseline. The consistency of the results
across all three approaches strengthens the credibility of our
conclusions: male employees and higher performers are more likely to be
promoted, whereas married employees face lower promotion odds. The
stability of these effects across model types shows that the observed
relationships are robust and not sensitive to model choice.
#Log-Likelihood & AIC comparison
logLik(logit_full)
## 'log Lik.' -52.88086 (df=7)
logLik(probit_full)
## 'log Lik.' -52.70076 (df=7)
AIC(logit_full, probit_full)
## df AIC
## logit_full 7 119.7617
## probit_full 7 119.4015
To compare the overall model fit between the logit and
probit specifications, we examine their log-likelihood
values and AIC scores. Both models produce very similar log-likelihoods,
indicating nearly identical explanatory power. The probit
model has a slightly lower AIC, suggesting a marginally better fit,
although the difference is too small to have practical relevance.
Overall, this comparison confirms that both nonlinear models perform
equally well, and our substantive conclusions do not depend on whether
we use logit or probit.
#VIF Check (Multikollinearität)
vif(logit_full)
## gender mean_overall_perf_z_score age
## 1.358939 1.338824 1.712404
## tenure higher_edu_indicator married
## 1.534909 1.082132 1.272267
Variance Inflation Factors (VIFs) are used to assess
multicollinearity among the predictors in the full model. All VIF values
are well below the common thresholds of 5 or 10, indicating that
multicollinearity is not a concern. This confirms that the included
variables—especially married and
children_indicator, which are highly correlated in the raw
data—do not create instability in the final model specification, as only
married is included. Overall, the predictors are
sufficiently independent for reliable coefficient estimation.
Now we check for the marginal effects:
probitmfx(probit_full, data = final_all, atmean = FALSE)
## Call:
## probitmfx(formula = probit_full, data = final_all, atmean = FALSE)
##
## Marginal Effects:
## dF/dx Std. Err. z P>|z|
## genderMale 0.1443070 0.0664766 2.1708 0.029947 *
## mean_overall_perf_z_score 0.1729997 0.0628049 2.7546 0.005877 **
## age -0.0063714 0.0126304 -0.5045 0.613945
## tenure 0.0028595 0.0018741 1.5259 0.127046
## higher_edu_indicatorHas Degree -0.0344023 0.0627802 -0.5480 0.583706
## married -0.1449074 0.0582619 -2.4872 0.012876 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## dF/dx is for discrete change for the following variables:
##
## [1] "genderMale" "higher_edu_indicatorHas Degree"
## [3] "married"
probitmfx(probit_full, data = final_all, atmean = TRUE)
## Call:
## probitmfx(formula = probit_full, data = final_all, atmean = TRUE)
##
## Marginal Effects:
## dF/dx Std. Err. z P>|z|
## genderMale 0.1421106 0.0668196 2.1268 0.033438 *
## mean_overall_perf_z_score 0.1682889 0.0616241 2.7309 0.006316 **
## age -0.0061979 0.0122808 -0.5047 0.613780
## tenure 0.0027817 0.0018494 1.5041 0.132552
## higher_edu_indicatorHas Degree -0.0333884 0.0609293 -0.5480 0.583702
## married -0.1369349 0.0535467 -2.5573 0.010549 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## dF/dx is for discrete change for the following variables:
##
## [1] "genderMale" "higher_edu_indicatorHas Degree"
## [3] "married"
There is also another function to check the marginal effects:
#Marginal Effects (Probit)
me_full <- margins(probit_full)
summary(me_full)
## factor AME SE z p lower upper
## age -0.0064 0.0126 -0.5044 0.6139 -0.0311 0.0184
## genderMale 0.1443 0.0665 2.1708 0.0299 0.0140 0.2746
## higher_edu_indicatorHas Degree -0.0344 0.0628 -0.5480 0.5837 -0.1574 0.0886
## married -0.1872 0.1006 -1.8613 0.0627 -0.3844 0.0099
## mean_overall_perf_z_score 0.1730 0.0628 2.7546 0.0059 0.0499 0.2961
## tenure 0.0029 0.0019 1.5259 0.1270 -0.0008 0.0065
In total the marginal effects show that genderMale
increases the probability of promotion by about 14 percentage points,
and performance raises it by roughly 17 points—both statistically
significant. Married reduces promotion chances by about 19
points and is marginally significant. Age, tenure, and higher education
have small, insignificant effects. These results confirm the main
findings from the Probit model.
#McFadden Pseudo-R²
pR2(probit_full)
## fitting null model for pseudo-r2
## llh llhNull G2 McFadden r2ML r2CU
## -52.7007599 -60.0143376 14.6271554 0.1218638 0.1026859 0.1743468
The McFadden Pseudo-R² of the probit model is about
0.12, which indicates a modest but meaningful improvement over the null
model. Values in this range are typical for discrete-choice models and
suggest that the included predictors provide reasonable explanatory
power. This aligns with earlier results showing that performance,
gender, and marital status drive most of the predictive strength in the
model.
Overall, the analysis identifies three main determinants of promotion: performance, gender, and marital status. First, individual performance is the strongest and most consistent predictor across all models, with higher-performing employees having substantially greater promotion probabilities. Second, once relevant controls are added, male employees exhibit significantly higher promotion chances, revealing omitted variable bias in the simple gender-only model. Third, marital status is negatively associated with promotion, even after controlling for performance and demographics.
Other factors such as age, tenure, and higher education show no
meaningful effects. Strong correlations between married and
children_indicator justify including only one of them in
the models to avoid multicollinearity. The OLS, logit, and
probit models yield consistent results, and model fit
metrics (AIC, log-likelihood, Pseudo-R²) confirm that the conclusions
are robust and not sensitive to estimation method. Marginal effects
reinforce these findings, showing sizable effects for gender,
performance, and marital status, while all other predictors remain small
and statistically insignificant.
Overall, the evidence suggests that promotion decisions in this dataset are driven primarily by productivity and demographic characteristics related to gender and marital status.
quitjob status to find
significant differences between employees who quit versus those who
stayed, using group_by() and skim().pct_on_site_weeks, mean_overall_perf_z_score,
children, median_gross_wage_cny,
costofcommute.quitjob. Both showed similar explanatory
power; Logit was chosen for easier interpretation.quitjob.# Finding the relevant variables.
final_all %>%
group_by(quitjob) %>%
skim()
| Name | Piped data |
| Number of rows | 135 |
| Number of columns | 36 |
| _______________________ | |
| Column type frequency: | |
| factor | 4 |
| numeric | 31 |
| ________________________ | |
| Group variables | quitjob |
Variable type: factor
| skim_variable | quitjob | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|---|
| gender | 0 | 0 | 1 | FALSE | 2 | Fem: 49, Mal: 46 |
| gender | 1 | 0 | 1 | FALSE | 2 | Mal: 22, Fem: 18 |
| higher_edu_indicator | 0 | 0 | 1 | FALSE | 2 | No : 57, Has: 38 |
| higher_edu_indicator | 1 | 0 | 1 | FALSE | 2 | No : 22, Has: 18 |
| bedroom_indicator | 0 | 0 | 1 | FALSE | 2 | Has: 92, No : 3 |
| bedroom_indicator | 1 | 0 | 1 | FALSE | 1 | Has: 40, No : 0 |
| children_indicator | 0 | 0 | 1 | FALSE | 2 | No : 84, Has: 11 |
| children_indicator | 1 | 0 | 1 | FALSE | 2 | No : 31, Has: 9 |
Variable type: numeric
| skim_variable | quitjob | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| personid | 0 | 0 | 1.00 | 31984.36 | 11160.87 | 4122.00 | 24190.00 | 36314.00 | 40396.00 | 45442.00 | ▁▂▂▃▇ |
| personid | 1 | 0 | 1.00 | 34456.30 | 9938.47 | 6278.00 | 29663.50 | 38852.00 | 41323.00 | 44794.00 | ▁▁▂▂▇ |
| age | 0 | 0 | 1.00 | 23.93 | 3.46 | 18.00 | 22.00 | 23.00 | 26.00 | 35.00 | ▅▇▆▂▁ |
| age | 1 | 0 | 1.00 | 23.65 | 3.08 | 19.00 | 22.00 | 23.00 | 25.00 | 32.00 | ▃▇▁▂▁ |
| tenure | 0 | 0 | 1.00 | 22.61 | 19.59 | 2.00 | 8.00 | 19.00 | 34.50 | 94.00 | ▇▃▂▁▁ |
| tenure | 1 | 0 | 1.00 | 18.59 | 17.61 | 2.50 | 6.00 | 10.00 | 25.25 | 77.00 | ▇▃▁▁▁ |
| commute | 0 | 0 | 1.00 | 104.35 | 64.46 | 20.00 | 52.50 | 80.00 | 180.00 | 300.00 | ▇▃▅▁▁ |
| commute | 1 | 0 | 1.00 | 108.17 | 73.14 | 10.00 | 40.00 | 102.50 | 145.00 | 300.00 | ▇▇▃▃▁ |
| married | 0 | 0 | 1.00 | 0.18 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| married | 1 | 0 | 1.00 | 0.30 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| promote_switch | 0 | 0 | 1.00 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| promote_switch | 1 | 0 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| costofcommute | 0 | 0 | 1.00 | 6.87 | 5.95 | 0.00 | 2.50 | 6.00 | 10.00 | 30.00 | ▇▆▁▁▁ |
| costofcommute | 1 | 0 | 1.00 | 8.66 | 9.57 | 0.00 | 2.75 | 7.00 | 10.00 | 55.00 | ▇▂▁▁▁ |
| mean_overall_perf_z_score | 0 | 0 | 1.00 | 0.02 | 0.53 | -1.16 | -0.25 | 0.03 | 0.38 | 1.42 | ▂▅▇▅▁ |
| mean_overall_perf_z_score | 1 | 0 | 1.00 | -0.28 | 0.50 | -1.20 | -0.61 | -0.33 | 0.07 | 0.81 | ▃▇▇▆▃ |
| mean_phonecall_perf_z_score | 0 | 0 | 1.00 | 0.02 | 0.46 | -1.12 | -0.23 | 0.05 | 0.32 | 1.08 | ▂▂▇▅▂ |
| mean_phonecall_perf_z_score | 1 | 0 | 1.00 | -0.18 | 0.54 | -1.08 | -0.57 | -0.24 | 0.16 | 1.22 | ▇▇▇▂▂ |
| total_calls | 0 | 0 | 1.00 | 34393.18 | 8578.34 | 8483.00 | 27893.00 | 35971.00 | 40552.00 | 49621.00 | ▁▂▂▇▃ |
| total_calls | 1 | 0 | 1.00 | 23846.47 | 7831.75 | 6088.00 | 20094.00 | 23130.00 | 28002.75 | 41178.00 | ▂▂▇▃▂ |
| mean_log_phone_calls | 0 | 0 | 1.00 | 6.02 | 0.19 | 5.31 | 5.95 | 6.06 | 6.15 | 6.47 | ▁▂▅▇▂ |
| mean_log_phone_calls | 1 | 0 | 1.00 | 5.94 | 0.23 | 5.33 | 5.77 | 5.92 | 6.07 | 6.59 | ▁▆▇▅▁ |
| mean_log_calls_per_second | 0 | 0 | 1.00 | -5.18 | 0.11 | -5.56 | -5.25 | -5.18 | -5.11 | -4.99 | ▁▁▅▇▅ |
| mean_log_calls_per_second | 1 | 1 | 0.98 | -5.15 | 0.13 | -5.44 | -5.24 | -5.15 | -5.09 | -4.81 | ▂▆▇▂▂ |
| mean_log_average_call_lenght | 0 | 0 | 1.00 | 11.20 | 0.19 | 10.45 | 11.11 | 11.22 | 11.32 | 11.67 | ▁▁▆▇▁ |
| mean_log_average_call_lenght | 1 | 1 | 0.98 | 11.07 | 0.19 | 10.54 | 10.98 | 11.07 | 11.23 | 11.38 | ▁▂▆▇▇ |
| mean_log_calls_per_day_worked | 0 | 0 | 1.00 | 9.49 | 0.20 | 8.67 | 9.39 | 9.49 | 9.61 | 9.91 | ▁▁▃▇▂ |
| mean_log_calls_per_day_worked | 1 | 1 | 0.98 | 9.41 | 0.19 | 8.91 | 9.33 | 9.44 | 9.55 | 9.70 | ▂▂▂▇▅ |
| mean_log_days_worked | 0 | 0 | 1.00 | 1.70 | 0.10 | 1.39 | 1.64 | 1.70 | 1.77 | 1.85 | ▁▂▇▆▇ |
| mean_log_days_worked | 1 | 0 | 1.00 | 1.63 | 0.10 | 1.34 | 1.60 | 1.62 | 1.67 | 1.78 | ▁▁▅▇▅ |
| weeks_observed | 0 | 0 | 1.00 | 78.62 | 10.72 | 27.00 | 72.00 | 84.00 | 86.00 | 86.00 | ▁▁▁▂▇ |
| weeks_observed | 1 | 0 | 1.00 | 60.02 | 12.39 | 31.00 | 51.75 | 59.50 | 67.25 | 86.00 | ▁▅▇▅▂ |
| remote_weeks | 0 | 0 | 1.00 | 18.18 | 17.05 | 0.00 | 0.00 | 25.00 | 35.50 | 39.00 | ▇▁▁▂▇ |
| remote_weeks | 1 | 0 | 1.00 | 5.28 | 9.98 | 0.00 | 0.00 | 0.00 | 6.00 | 39.00 | ▇▁▁▁▁ |
| on_site_weeks | 0 | 0 | 1.00 | 60.44 | 18.30 | 26.00 | 47.00 | 57.00 | 81.50 | 86.00 | ▂▇▃▂▆ |
| on_site_weeks | 1 | 0 | 1.00 | 54.75 | 11.74 | 31.00 | 47.00 | 51.50 | 61.50 | 84.00 | ▁▇▃▃▁ |
| pct_remote_weeks | 0 | 0 | 1.00 | 0.23 | 0.21 | 0.00 | 0.00 | 0.30 | 0.44 | 0.58 | ▇▁▁▇▁ |
| pct_remote_weeks | 1 | 0 | 1.00 | 0.08 | 0.13 | 0.00 | 0.00 | 0.00 | 0.11 | 0.45 | ▇▁▁▁▁ |
| pct_on_site_weeks | 0 | 0 | 1.00 | 0.77 | 0.21 | 0.42 | 0.56 | 0.70 | 1.00 | 1.00 | ▁▇▁▁▇ |
| pct_on_site_weeks | 1 | 0 | 1.00 | 0.92 | 0.13 | 0.55 | 0.89 | 1.00 | 1.00 | 1.00 | ▁▁▁▁▇ |
| median_base_wage_cny | 0 | 0 | 1.00 | 1652.11 | 117.18 | 1300.00 | 1550.00 | 1650.00 | 1750.00 | 1900.00 | ▁▃▇▅▃ |
| median_base_wage_cny | 1 | 0 | 1.00 | 1596.25 | 150.91 | 1400.00 | 1500.00 | 1575.00 | 1631.25 | 2375.00 | ▇▇▁▁▁ |
| median_gross_wage_cny | 0 | 0 | 1.00 | 3210.21 | 682.06 | 1919.76 | 2646.28 | 3095.48 | 3710.67 | 4801.74 | ▂▇▆▃▂ |
| median_gross_wage_cny | 1 | 0 | 1.00 | 2580.22 | 470.84 | 2047.50 | 2229.62 | 2455.95 | 2712.75 | 4228.00 | ▇▃▂▁▁ |
| median_bonus_total_cny | 0 | 0 | 1.00 | 1562.07 | 592.09 | 517.76 | 1095.99 | 1449.84 | 1974.90 | 3051.98 | ▅▇▅▃▂ |
| median_bonus_total_cny | 1 | 0 | 1.00 | 1016.70 | 372.66 | 597.50 | 737.04 | 918.25 | 1131.23 | 2128.84 | ▇▅▂▁▁ |
| months_observed | 0 | 0 | 1.00 | 25.25 | 1.60 | 20.00 | 26.00 | 26.00 | 26.00 | 26.00 | ▁▁▁▁▇ |
| months_observed | 1 | 0 | 1.00 | 15.20 | 2.95 | 7.00 | 13.00 | 15.00 | 16.25 | 20.00 | ▁▃▅▇▅ |
| exhaustion | 0 | 34 | 0.64 | 8.59 | 6.53 | 0.00 | 3.00 | 9.00 | 12.00 | 29.00 | ▇▇▅▁▁ |
| exhaustion | 1 | 40 | 0.00 | NaN | NA | NA | NA | NA | NA | NA | |
| negative | 0 | 34 | 0.64 | 16.64 | 5.39 | 8.00 | 12.00 | 17.00 | 20.00 | 34.00 | ▇▆▇▁▁ |
| negative | 1 | 40 | 0.00 | NaN | NA | NA | NA | NA | NA | NA | |
| positive | 0 | 34 | 0.64 | 24.25 | 5.14 | 13.00 | 21.00 | 24.00 | 27.00 | 37.00 | ▂▆▇▅▁ |
| positive | 1 | 40 | 0.00 | NaN | NA | NA | NA | NA | NA | NA | |
| log_wage | 0 | 0 | 1.00 | 8.05 | 0.21 | 7.56 | 7.88 | 8.04 | 8.22 | 8.48 | ▁▇▆▅▃ |
| log_wage | 1 | 0 | 1.00 | 7.84 | 0.17 | 7.62 | 7.71 | 7.81 | 7.91 | 8.35 | ▇▇▃▂▁ |
| log_bonus | 0 | 0 | 1.00 | 7.28 | 0.39 | 6.25 | 7.00 | 7.28 | 7.59 | 8.02 | ▂▅▇▇▆ |
| log_bonus | 1 | 0 | 1.00 | 6.87 | 0.33 | 6.39 | 6.60 | 6.82 | 7.03 | 7.66 | ▇▇▅▃▂ |
| log_tenure | 0 | 0 | 1.00 | 2.67 | 1.04 | 0.69 | 2.08 | 2.94 | 3.54 | 4.54 | ▅▅▅▇▆ |
| log_tenure | 1 | 0 | 1.00 | 2.52 | 0.93 | 0.92 | 1.79 | 2.30 | 3.23 | 4.34 | ▅▇▅▇▃ |
| promotion | 0 | 0 | 1.00 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| promotion | 1 | 0 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
Looking at those who quit vs those who didn’t, we see differences
that might be interesting in: pct_on_site_weeks,
mean_overall_perf_z_score, children,
median_gross_wage_cny, costofcommute.
# Plotting the correlations between the numeric variables
mini_cor_matrix <- final_all %>%
dplyr::select(quitjob, pct_on_site_weeks, children_indicator, median_gross_wage_cny, costofcommute) %>%
mutate(across(everything(), as.numeric)) %>%
correlate(diagonal = 1) %>%
rearrange() %>% shave()
## Correlation computed with
## • Method: 'pearson'
## • Missing treated using: 'pairwise.complete.obs'
mini_cor_matrix
## # A tibble: 5 × 6
## term quitjob pct_on_site_weeks costofcommute children_indicator
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 quitjob 1 NA NA NA
## 2 pct_on_site_weeks 0.339 1 NA NA
## 3 costofcommute 0.114 -0.0262 1 NA
## 4 children_indicator 0.140 0.0500 0.209 1
## 5 median_gross_wage_… -0.419 -0.223 -0.255 0.197
## # ℹ 1 more variable: median_gross_wage_cny <dbl>
Significant values between quitjob and the independent
variables, especially pct_on_site_weeks and
median_gross_wage_cny, especially since
quitjob is binary. Low correlation between the independent
variables, so no multicollinearity issues.
# Initial Logit and Probit Models
mini_attrition <- quitjob ~ pct_on_site_weeks + mean_overall_perf_z_score + children_indicator + median_gross_wage_cny + costofcommute
logit_mini_attrition <- glm(mini_attrition,
data = final_all,
family = binomial(link = "logit"))
probit_mini_attrition <- glm(mini_attrition,
data = final_all,
family = binomial(link = "probit"))
stargazer(final_all, logit_mini_attrition, probit_mini_attrition,
type = "text",
title = "Regression Results: Mini Attrition Model",
no.space = TRUE, header = FALSE, font.size = 'scriptsize')
##
## Regression Results: Mini Attrition Model
## =================================
## Statistic N Mean St. Dev. Min Max
## =================================
##
## Regression Results: Mini Attrition Model
## =================================================================
## Dependent variable:
## ----------------------------
## quitjob
## logistic probit
## (1) (2)
## -----------------------------------------------------------------
## pct_on_site_weeks 3.786*** 2.177***
## (1.421) (0.767)
## mean_overall_perf_z_score -1.049* -0.567*
## (0.542) (0.302)
## children_indicatorHas Child/Children 2.677*** 1.516***
## (0.870) (0.475)
## median_gross_wage_cny -0.003*** -0.001***
## (0.001) (0.0003)
## costofcommute -0.007 -0.005
## (0.036) (0.020)
## Constant 2.491 1.364
## (2.204) (1.209)
## -----------------------------------------------------------------
## Observations 135 135
## Log Likelihood -54.251 -54.585
## Akaike Inf. Crit. 120.501 121.170
## =================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
costofcommute is not significant in both models, so we
can drop it. The other variables are significant in both models.
# Final Models without costofcommute
mini_attrition <- quitjob ~ pct_on_site_weeks + mean_overall_perf_z_score + children_indicator + median_gross_wage_cny
logit_mini_attrition <- glm(mini_attrition,
data = final_all,
family = binomial(link = "logit"))
probit_mini_attrition <- glm(mini_attrition,
data = final_all,
family = binomial(link = "probit"))
stargazer(final_all, logit_mini_attrition, probit_mini_attrition,
type = "text",
title = "Regression Results: Mini Attrition Model",
no.space = TRUE, header = FALSE, font.size = 'scriptsize')
##
## Regression Results: Mini Attrition Model
## =================================
## Statistic N Mean St. Dev. Min Max
## =================================
##
## Regression Results: Mini Attrition Model
## =================================================================
## Dependent variable:
## ----------------------------
## quitjob
## logistic probit
## (1) (2)
## -----------------------------------------------------------------
## pct_on_site_weeks 3.834*** 2.204***
## (1.406) (0.760)
## mean_overall_perf_z_score -1.066** -0.576*
## (0.537) (0.300)
## children_indicatorHas Child/Children 2.622*** 1.477***
## (0.826) (0.450)
## median_gross_wage_cny -0.002*** -0.001***
## (0.001) (0.0003)
## Constant 2.294 1.243
## (1.983) (1.090)
## -----------------------------------------------------------------
## Observations 135 135
## Log Likelihood -54.272 -54.613
## Akaike Inf. Crit. 118.544 119.225
## =================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
pR2(logit_mini_attrition)
## fitting null model for pseudo-r2
## llh llhNull G2 McFadden r2ML r2CU
## -54.2720157 -82.0386122 55.5331930 0.3384577 0.3372497 0.4794533
pR2(probit_mini_attrition)
## fitting null model for pseudo-r2
## llh llhNull G2 McFadden r2ML r2CU
## -54.6127435 -82.0386122 54.8517374 0.3343044 0.3338958 0.4746852
Similar values, pick either. We will use LOGIT since intepretation is more linear.. All of the variables within the model have a high statistical significance. Joining them in a table for easy interpretation.
# Marginal Effects for Logit Model
marg_summary_mini <- summary(margins(logit_mini_attrition))
# coefficient table
coef_tbl_mini <- broom::tidy(logit_mini_attrition) %>%
mutate(
odds_ratio = exp(estimate),
lower_OR = exp(estimate - 1.96 * std.error),
upper_OR = exp(estimate + 1.96 * std.error)
)
# marginal effects table
marg_tbl_mini <- marg_summary_mini %>%
rename(
term = factor,
AME = AME,
AME_se = SE,
AME_p = p,
AME_lower = lower,
AME_upper = upper
)
# join both tables
logit_attrition_table_mini <- coef_tbl_mini %>%
full_join(marg_tbl_mini, by = "term") %>%
dplyr::select(term, estimate, std.error, p.value,
odds_ratio, lower_OR, upper_OR,
AME, AME_lower, AME_upper)
knitr::kable(logit_attrition_table_mini)
| term | estimate | std.error | p.value | odds_ratio | lower_OR | upper_OR | AME | AME_lower | AME_upper |
|---|---|---|---|---|---|---|---|---|---|
| (Intercept) | 2.2941424 | 1.9833681 | 0.2473990 | 9.9159280 | 0.2032621 | 483.7381957 | NA | NA | NA |
| pct_on_site_weeks | 3.8344321 | 1.4059793 | 0.0063868 | 46.2671434 | 2.9408914 | 727.8910609 | 0.4995854 | 0.1771306 | 0.8220402 |
| mean_overall_perf_z_score | -1.0657751 | 0.5372069 | 0.0472648 | 0.3444608 | 0.1201877 | 0.9872326 | -0.1388591 | -0.2679113 | -0.0098069 |
| children_indicatorHas Child/Children | 2.6219705 | 0.8255256 | 0.0014926 | 13.7628163 | 2.7290139 | 69.4078970 | 0.3578774 | 0.1768680 | 0.5388868 |
| median_gross_wage_cny | -0.0024851 | 0.0005909 | 0.0000260 | 0.9975179 | 0.9963633 | 0.9986739 | -0.0003238 | -0.0004310 | -0.0002166 |
pct_on_site_weeks
Going from full remote to full on-site work (0% to 100% on-site weeks)
increases the probability of quitting by 50%.
The odds of quitting are 46.3× higher for employees who work on
site.
mean_overall_perf_z_score
The odds of quitting drop by 65.6% for each 1-standard-deviation
increase in performance (1 − 0.344 = 0.656).
On average, a one–SD increase in performance reduces the probability of
quitting by 13.9 percentage points.
children
The odds of quitting are 13.8× higher for employees with children.
Having children increases the probability of quitting by 35.8 percentage
points, on average.
This effect is large, statistically significant, and precisely
estimated.
median_gross_wage_cny
Each 1-unit increase in wage (1 CNY) reduces the odds of quitting by
about 0.02%.
In probability terms, each additional 1 CNY reduces quitting probability
by 0.000324 (0.03 percentage points).
While the effect is statistically significant, it is very small in
magnitude.
Practical interpretation:
A 100 CNY increase in wage reduces quitting probability by 3.24
percentage points.
A 1000 CNY increase in wage reduces quitting probability by 32.4
percentage points.
To summarize:
The main driver of quitting is whether employees work on site versus
remotely.
Performance is also a strong predictor of quitting, although improving
performance is more challenging than offering remote work options.
Having children is an important predictor, likely reflecting increased
need for flexibility.
Wage has a statistically significant but very small effect, meaning wage
increases may not be an efficient strategy to prevent quitting except
when justified by other factors.
# On site impact
logit_mini_attrition %>%
ggeffects::ggpredict(terms = "pct_on_site_weeks [all]") %>%
plot() +
labs(
title = "Predicted Probability of Quitting vs. Percent On-Site Weeks",
x = "Percent On-Site Weeks",
y = "Predicted Probability of Quitting"
) +
theme_minimal()
# Performance impact
logit_mini_attrition %>%
ggeffects::ggpredict(terms = "mean_overall_perf_z_score [all]") %>%
plot() +
labs(
title = "Predicted Probability of Quitting vs. Mean Overall Performance Z-Score",
x = "Mean Overall Performance Z-Score",
y = "Predicted Probability of Quitting"
) +
theme_minimal()
# Wage impact
logit_mini_attrition %>%
ggeffects::ggpredict(terms = "median_gross_wage_cny [all]") %>%
plot() +
labs(
title = "Predicted Probability of Quitting vs. Median Gross Wage (CNY)",
x = "Median Gross Wage (CNY)",
y = "Predicted Probability of Quitting"
) +
theme_minimal()
This again reinforces the idea of WFH being the strongest factor, aswell as highlighting how the other variables also impact quitting probability.
Having children is not visualized since its a binary variable.
WFH:
The variables that might explain Attrition and WFH : Exhaustion and Negative. Performance attributes dont influence quitting.
final_panel_weekly %>%
group_by(WFH_due_building_issues) %>%
skim()
| Name | Piped data |
| Number of rows | 10079 |
| Number of columns | 31 |
| _______________________ | |
| Column type frequency: | |
| Date | 2 |
| factor | 4 |
| numeric | 24 |
| ________________________ | |
| Group variables | WFH_due_building_issues |
Variable type: Date
| skim_variable | WFH_due_building_issues | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|---|
| date | On-site | 0 | 1 | 2022-01-03 | 2023-08-14 | 2022-08-22 | 85 |
| date | Remote | 0 | 1 | 2022-11-28 | 2023-08-14 | 2023-03-27 | 38 |
| date | NA | 0 | 1 | 2022-12-05 | 2023-08-28 | 2023-08-21 | 33 |
| month | On-site | 0 | 1 | 2022-01-01 | 2023-08-01 | 2022-08-01 | 20 |
| month | Remote | 0 | 1 | 2022-11-01 | 2023-08-01 | 2023-03-01 | 10 |
| month | NA | 0 | 1 | 2022-12-01 | 2023-08-01 | 2023-08-01 | 9 |
Variable type: factor
| skim_variable | WFH_due_building_issues | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|---|
| gender | On-site | 0 | 1 | FALSE | 2 | Fem: 4030, Mal: 3902 |
| gender | Remote | 0 | 1 | FALSE | 2 | Mal: 1027, Fem: 911 |
| gender | NA | 0 | 1 | FALSE | 2 | Mal: 135, Fem: 74 |
| higher_edu_indicator | On-site | 0 | 1 | FALSE | 2 | No : 4779, Has: 3153 |
| higher_edu_indicator | Remote | 0 | 1 | FALSE | 2 | No : 1142, Has: 796 |
| higher_edu_indicator | NA | 0 | 1 | FALSE | 2 | No : 130, Has: 79 |
| bedroom_indicator | On-site | 0 | 1 | FALSE | 2 | Has: 7741, No : 191 |
| bedroom_indicator | Remote | 0 | 1 | FALSE | 2 | Has: 1871, No : 67 |
| bedroom_indicator | NA | 0 | 1 | FALSE | 2 | Has: 203, No : 6 |
| children_indicator | On-site | 0 | 1 | FALSE | 2 | No : 6778, Has: 1154 |
| children_indicator | Remote | 0 | 1 | FALSE | 2 | No : 1683, Has: 255 |
| children_indicator | NA | 0 | 1 | FALSE | 2 | No : 197, Has: 12 |
Variable type: numeric
| skim_variable | WFH_due_building_issues | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| personid | On-site | 0 | 1.00 | 32577.69 | 10564.39 | 4122.00 | 26328.00 | 36908.00 | 40322.00 | 45442.00 | ▁▂▂▃▇ |
| personid | Remote | 0 | 1.00 | 32150.19 | 10859.48 | 4122.00 | 24324.00 | 36908.00 | 40472.00 | 44784.00 | ▁▂▃▂▇ |
| personid | NA | 0 | 1.00 | 33705.25 | 10066.62 | 4122.00 | 27704.00 | 36032.00 | 42152.00 | 45442.00 | ▁▂▂▅▇ |
| year | On-site | 0 | 1.00 | 2022.22 | 0.41 | 2022.00 | 2022.00 | 2022.00 | 2022.00 | 2023.00 | ▇▁▁▁▂ |
| year | Remote | 0 | 1.00 | 2022.85 | 0.36 | 2022.00 | 2023.00 | 2023.00 | 2023.00 | 2023.00 | ▂▁▁▁▇ |
| year | NA | 0 | 1.00 | 2022.96 | 0.19 | 2022.00 | 2023.00 | 2023.00 | 2023.00 | 2023.00 | ▁▁▁▁▇ |
| week | On-site | 0 | 1.00 | 24.47 | 14.30 | 1.00 | 12.00 | 24.00 | 36.00 | 53.00 | ▇▇▇▆▅ |
| week | Remote | 0 | 1.00 | 21.20 | 15.22 | 1.00 | 9.00 | 18.00 | 29.00 | 53.00 | ▇▆▆▁▃ |
| week | NA | 0 | 1.00 | 27.75 | 11.39 | 2.00 | 18.00 | 34.00 | 35.00 | 53.00 | ▂▂▁▇▁ |
| exhaustion | On-site | 6906 | 0.13 | 10.68 | 8.84 | 0.00 | 4.00 | 10.00 | 14.00 | 36.00 | ▇▇▃▁▁ |
| exhaustion | Remote | 794 | 0.59 | 6.66 | 5.99 | 0.00 | 0.00 | 6.00 | 11.00 | 36.00 | ▇▅▁▁▁ |
| exhaustion | NA | 0 | 1.00 | 9.13 | 8.36 | 0.00 | 2.00 | 7.00 | 13.00 | 36.00 | ▇▅▂▁▁ |
| negative | On-site | 6906 | 0.13 | 18.16 | 7.09 | 8.00 | 12.00 | 18.00 | 23.00 | 40.00 | ▇▇▇▁▁ |
| negative | Remote | 794 | 0.59 | 15.26 | 6.23 | 8.00 | 10.00 | 15.00 | 19.00 | 40.00 | ▇▆▂▁▁ |
| negative | NA | 0 | 1.00 | 16.90 | 7.29 | 8.00 | 11.00 | 16.00 | 21.00 | 40.00 | ▇▇▃▁▁ |
| positive | On-site | 6906 | 0.13 | 22.58 | 6.33 | 8.00 | 18.25 | 24.00 | 27.00 | 40.00 | ▂▅▇▅▁ |
| positive | Remote | 794 | 0.59 | 25.61 | 6.63 | 8.00 | 22.00 | 26.00 | 30.00 | 40.00 | ▁▃▇▅▂ |
| positive | NA | 0 | 1.00 | 24.61 | 6.73 | 8.00 | 21.00 | 24.00 | 29.00 | 40.00 | ▁▃▇▅▁ |
| perform1 | On-site | 22 | 1.00 | -0.06 | 1.00 | -3.03 | -0.65 | 0.02 | 0.59 | 4.16 | ▁▆▇▁▁ |
| perform1 | Remote | 1 | 1.00 | 0.15 | 0.91 | -2.99 | -0.44 | 0.13 | 0.73 | 3.37 | ▁▃▇▃▁ |
| perform1 | NA | 209 | 0.00 | NaN | NA | NA | NA | NA | NA | NA | |
| phonecall | On-site | 133 | 0.98 | -0.04 | 0.99 | -3.11 | -0.57 | 0.05 | 0.59 | 5.83 | ▁▇▃▁▁ |
| phonecall | Remote | 1 | 1.00 | 0.10 | 0.85 | -3.10 | -0.43 | 0.12 | 0.65 | 3.37 | ▁▃▇▃▁ |
| phonecall | NA | 209 | 0.00 | NaN | NA | NA | NA | NA | NA | NA | |
| phonecallraw | On-site | 277 | 0.97 | 438.56 | 143.56 | 1.00 | 355.00 | 444.00 | 526.00 | 1264.00 | ▁▇▃▁▁ |
| phonecallraw | Remote | 4 | 1.00 | 446.77 | 138.20 | 2.00 | 361.00 | 449.00 | 529.75 | 1024.00 | ▁▆▇▂▁ |
| phonecallraw | NA | 209 | 0.00 | NaN | NA | NA | NA | NA | NA | NA | |
| logphonecall | On-site | 277 | 0.97 | 6.00 | 0.49 | 0.00 | 5.87 | 6.10 | 6.27 | 7.14 | ▁▁▁▁▇ |
| logphonecall | Remote | 4 | 1.00 | 6.04 | 0.43 | 0.69 | 5.89 | 6.11 | 6.27 | 6.93 | ▁▁▁▁▇ |
| logphonecall | NA | 209 | 0.00 | NaN | NA | NA | NA | NA | NA | NA | |
| logcallpersec | On-site | 273 | 0.97 | -5.18 | 0.15 | -5.95 | -5.27 | -5.18 | -5.09 | -3.18 | ▁▇▁▁▁ |
| logcallpersec | Remote | 6 | 1.00 | -5.13 | 0.19 | -5.65 | -5.22 | -5.13 | -5.05 | -1.10 | ▇▁▁▁▁ |
| logcallpersec | NA | 209 | 0.00 | NaN | NA | NA | NA | NA | NA | NA | |
| logcalllength | On-site | 273 | 0.97 | 11.18 | 0.53 | 3.18 | 11.06 | 11.28 | 11.45 | 12.12 | ▁▁▁▁▇ |
| logcalllength | Remote | 6 | 1.00 | 11.16 | 0.48 | 2.48 | 11.03 | 11.24 | 11.42 | 12.00 | ▁▁▁▁▇ |
| logcalllength | NA | 209 | 0.00 | NaN | NA | NA | NA | NA | NA | NA | |
| logcall_dayworked | On-site | 273 | 0.97 | 9.49 | 0.46 | 2.48 | 9.35 | 9.57 | 9.74 | 10.36 | ▁▁▁▁▇ |
| logcall_dayworked | Remote | 6 | 1.00 | 9.43 | 0.43 | 2.48 | 9.27 | 9.47 | 9.67 | 10.21 | ▁▁▁▁▇ |
| logcall_dayworked | NA | 209 | 0.00 | NaN | NA | NA | NA | NA | NA | NA | |
| logdaysworked | On-site | 0 | 1.00 | 1.67 | 0.30 | 0.00 | 1.61 | 1.79 | 1.79 | 1.95 | ▁▁▁▁▇ |
| logdaysworked | Remote | 0 | 1.00 | 1.74 | 0.20 | 0.00 | 1.61 | 1.79 | 1.79 | 1.95 | ▁▁▁▁▇ |
| logdaysworked | NA | 209 | 0.00 | NaN | NA | NA | NA | NA | NA | NA | |
| basewage | On-site | 12 | 1.00 | 1573.43 | 171.53 | 650.00 | 1500.00 | 1550.00 | 1650.00 | 2450.00 | ▁▁▇▂▁ |
| basewage | Remote | 0 | 1.00 | 1696.39 | 146.62 | 1300.00 | 1600.00 | 1700.00 | 1750.00 | 2300.00 | ▂▇▆▁▁ |
| basewage | NA | 0 | 1.00 | 1794.50 | 176.38 | 1500.00 | 1650.00 | 1800.00 | 1900.00 | 2150.00 | ▅▇▆▆▃ |
| grosswage | On-site | 12 | 1.00 | 3016.97 | 932.15 | 1264.43 | 2409.28 | 2804.00 | 3432.97 | 14553.00 | ▇▂▁▁▁ |
| grosswage | Remote | 0 | 1.00 | 3343.48 | 1103.61 | 1630.00 | 2616.00 | 3102.79 | 3852.00 | 11953.59 | ▇▃▁▁▁ |
| grosswage | NA | 0 | 1.00 | 3759.56 | 1257.84 | 1740.00 | 3056.00 | 3445.00 | 4332.00 | 14553.00 | ▇▃▁▁▁ |
| bonustotal | On-site | 12 | 1.00 | 1443.54 | 860.02 | 0.00 | 894.00 | 1226.00 | 1822.10 | 12853.00 | ▇▁▁▁▁ |
| bonustotal | Remote | 0 | 1.00 | 1647.09 | 1045.57 | 5.00 | 982.81 | 1410.04 | 2114.67 | 10303.59 | ▇▃▁▁▁ |
| bonustotal | NA | 0 | 1.00 | 1965.06 | 1173.84 | 40.00 | 1324.00 | 1690.90 | 2444.00 | 12853.00 | ▇▂▁▁▁ |
| promote_switch | On-site | 0 | 1.00 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| promote_switch | Remote | 0 | 1.00 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| promote_switch | NA | 0 | 1.00 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| quitjob | On-site | 0 | 1.00 | 0.28 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| quitjob | Remote | 0 | 1.00 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| quitjob | NA | 0 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| costofcommute | On-site | 0 | 1.00 | 7.23 | 6.76 | 0.00 | 3.00 | 6.00 | 10.00 | 55.00 | ▇▂▁▁▁ |
| costofcommute | Remote | 0 | 1.00 | 7.55 | 9.03 | 0.00 | 0.00 | 5.00 | 10.00 | 55.00 | ▇▂▁▁▁ |
| costofcommute | NA | 0 | 1.00 | 6.58 | 5.78 | 0.00 | 0.00 | 5.00 | 10.00 | 30.00 | ▇▆▁▁▁ |
| age | On-site | 0 | 1.00 | 23.67 | 3.28 | 18.00 | 22.00 | 23.00 | 25.00 | 35.00 | ▅▇▆▁▁ |
| age | Remote | 0 | 1.00 | 24.45 | 3.59 | 19.00 | 22.00 | 23.00 | 26.00 | 35.00 | ▇▇▃▂▁ |
| age | NA | 0 | 1.00 | 23.82 | 3.43 | 18.00 | 22.00 | 23.00 | 26.00 | 34.00 | ▅▇▅▂▁ |
| tenure | On-site | 0 | 1.00 | 21.65 | 18.58 | 2.00 | 8.00 | 15.00 | 31.00 | 94.00 | ▇▃▂▁▁ |
| tenure | Remote | 0 | 1.00 | 22.34 | 19.71 | 2.00 | 8.00 | 15.00 | 34.00 | 94.00 | ▇▅▂▁▁ |
| tenure | NA | 0 | 1.00 | 19.92 | 17.35 | 2.00 | 5.00 | 19.00 | 28.00 | 94.00 | ▇▆▂▁▁ |
| commute | On-site | 0 | 1.00 | 106.79 | 66.12 | 10.00 | 55.00 | 90.00 | 170.00 | 300.00 | ▇▅▅▂▁ |
| commute | Remote | 0 | 1.00 | 91.80 | 63.49 | 20.00 | 40.00 | 60.00 | 135.00 | 300.00 | ▇▂▂▁▁ |
| commute | NA | 0 | 1.00 | 100.45 | 64.47 | 20.00 | 40.00 | 80.00 | 140.00 | 300.00 | ▇▃▃▁▁ |
| married | On-site | 0 | 1.00 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| married | Remote | 0 | 1.00 | 0.18 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| married | NA | 0 | 1.00 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
exhaustion.WFH.model <- lm(exhaustion ~ WFH_due_building_issues,
data = final_panel_weekly)
stargazer(exhaustion.WFH.model,
type = "text",
title = "Regression Results: Exhaustion and WFH",
no.space = TRUE, header = FALSE, font.size = 'scriptsize')
##
## Regression Results: Exhaustion and WFH
## =========================================================
## Dependent variable:
## ---------------------------
## exhaustion
## ---------------------------------------------------------
## WFH_due_building_issuesRemote -4.019***
## (0.321)
## Constant 10.681***
## (0.233)
## ---------------------------------------------------------
## Observations 2,170
## R2 0.067
## Adjusted R2 0.067
## Residual Std. Error 7.475 (df = 2168)
## F Statistic 156.352*** (df = 1; 2168)
## =========================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
mean(final_panel_weekly$exhaustion, na.rm = TRUE)
## [1] 8.612863
Very relevant results.
plot.attrition.df <- final_panel_weekly %>%
filter(!is.na(WFH_due_building_issues),
!is.na(exhaustion))
ggplot(plot.attrition.df, aes( y = exhaustion, fill = WFH_due_building_issues)) +
geom_boxplot() +
facet_wrap(~ WFH_due_building_issues) +
labs(
x = "WFH due to building issues",
y = "Exhaustion"
) +
ggtitle("Boxplot of Exhaustion by WFH Status") + theme_bw()
WFH is related to Exhaustion. Give more WFH days to fix exhaustion.
Children:
Factors to influence attrition: Exhaustion.
children.exhaustion.model <- lm(exhaustion ~ children_indicator,
data = final_panel_weekly)
stargazer(children.exhaustion.model,
type = "text",
title = "Regression Results: Exhaustion and Children",
no.space = TRUE, header = FALSE, font.size = 'scriptsize')
##
## Regression Results: Exhaustion and Children
## ================================================================
## Dependent variable:
## ---------------------------
## exhaustion
## ----------------------------------------------------------------
## children_indicatorHas Child/Children 1.517***
## (0.582)
## Constant 8.489***
## (0.167)
## ----------------------------------------------------------------
## Observations 2,379
## R2 0.003
## Adjusted R2 0.002
## Residual Std. Error 7.785 (df = 2377)
## F Statistic 6.794*** (df = 1; 2377)
## ================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
mean(final_panel_weekly$exhaustion, na.rm = TRUE)
## [1] 8.612863
Relevant to mention having kids increases exhaustion.
plot.children.df <- final_panel_weekly %>%
filter(!is.na(children_indicator),
!is.na(exhaustion))
ggplot(plot.children.df, aes( y = exhaustion, fill = children_indicator)) +
geom_boxplot() +
facet_wrap(~ children_indicator) +
labs(
x = "Has Children",
y = "Exhaustion"
) +
ggtitle("Boxplot of Exhaustion by Children Status") + theme_bw()
To fix this, we might give work from home options to employees with children have a daycare present?
Although there is no direct data to relate quitting and exhaustion, since the ones who quit are from two groups (children == YES and WFH == NO) and these groups have significant higher exhaustion levels, we can assume that exhaustion is a key driver in quitting. To reduce quitting, we need to reduce exhaustion.
final_panel_weekly %>%
group_by(quitjob, WFH_due_building_issues) %>%
summarise(n())
## `summarise()` has grouped output by 'quitjob'. You can override using the
## `.groups` argument.
## # A tibble: 5 × 3
## # Groups: quitjob [2]
## quitjob WFH_due_building_issues `n()`
## <dbl> <fct> <int>
## 1 0 On-site 5742
## 2 0 Remote 1727
## 3 0 <NA> 209
## 4 1 On-site 2190
## 5 1 Remote 211
final_panel_weekly %>%
filter(!is.na(quitjob),
!is.na(WFH_due_building_issues)) %>%
tabyl(quitjob, WFH_due_building_issues) %>%
adorn_totals(c('row', 'col')) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns('rear')
## quitjob On-site Remote Total
## 0 76.9% (5,742) 23.1% (1,727) 100.0% (7,469)
## 1 91.2% (2,190) 8.8% (211) 100.0% (2,401)
## Total 80.4% (7,932) 19.6% (1,938) 100.0% (9,870)
final_all %>%
filter(!is.na(quitjob),
!is.na(children_indicator)) %>%
tabyl(quitjob, children_indicator) %>%
adorn_totals(c('row', 'col')) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns('rear')
## quitjob No Children Has Child/Children Total
## 0 88.4% (84) 11.6% (11) 100.0% (95)
## 1 77.5% (31) 22.5% (9) 100.0% (40)
## Total 85.2% (115) 14.8% (20) 100.0% (135)
WFH is truly the key driver for quitting.
Looking at the Difference-in-Differences for
WFH_due_building_issues to understand the impact this event
had on the performance of employees who were forced to work from home
due to building issues. The execution follows the steps learned in Lab
7.
WFH_due_building_issues (forced to
work remotely) versus comparable on-site workers who remained
unaffected.Post (after building issues) ×
Treated (WFH_due_building_issues), with the
pre-period capturing the baseline when all relevant employees were
on-site.First we create the DiD panel data to ensure that there are no
NA values for performance.
# Creating the DiD panel data
panel_DiD <- final_panel_weekly %>%
filter(date >= as.Date("2022-01-01"),
date <= as.Date("2023-08-14")) %>%
mutate(
post = ifelse(date >= as.Date("2022-12-12"), 1, 0)
)
Next we create the treatment variable based on whether the person
ever had WFH_due_building_issues during the period.
#define group that gets treated (treat)
#treat = 1, if person has to switch to home office because of building issues
#otherwise = 0
panel_DiD <- panel_DiD %>%
group_by(personid) %>%
mutate(
treat = ifelse(any(WFH_due_building_issues == "Remote"), 1, 0)
) %>%
ungroup()
# Check if we have both groups in the periods
table(panel_DiD$treat, panel_DiD$post)
##
## 0 1
## 0 2316 1204
## 1 3068 2198
we then create a missing variable to identify individuals with missing performance in any period or not enough periods (in this case less than 2)
missing_by_person <- panel_DiD %>%
group_by(personid) %>%
summarise(
n_periods = n_distinct(post),
any_na = any(is.na(perform1)),
missing = ifelse(n_periods < 2 | any_na, 1, 0),
.groups = "drop"
)
panel_DiD <- panel_DiD %>%
left_join(missing_by_person, by = "personid")
# Balance-Overview
table(panel_DiD$missing)
##
## 0 1
## 7220 2737
table(panel_DiD$treat, panel_DiD$missing)
##
## 0 1
## 0 3392 128
## 1 3828 1438
Now we create the balanced panel by filtering out individuals with
missing performance in any period or not enough
periods.
panel_balanced <- panel_DiD %>%
filter(missing == 0)
# check
table(panel_balanced$treat, panel_balanced$post)
##
## 0 1
## 0 2239 1153
## 1 2293 1535
Now we can calculate the means for each group and period to see the differences.
#Calculating DiD_means
did_means <- panel_balanced %>%
group_by(treat, post) %>%
summarise(
mean_perf = mean(perform1, na.rm = TRUE),
n = n(),
.groups = "drop"
)
did_means
## # A tibble: 4 × 4
## treat post mean_perf n
## <dbl> <dbl> <dbl> <int>
## 1 0 0 -0.00184 2239
## 2 0 1 -0.157 1153
## 3 1 0 -0.00173 2293
## 4 1 1 0.171 1535
Now we can calculate the DiD estimate manually
treat_pre <- did_means$mean_perf[did_means$treat == 1 & did_means$post == 0]
treat_post <- did_means$mean_perf[did_means$treat == 1 & did_means$post == 1]
ctrl_pre <- did_means$mean_perf[did_means$treat == 0 & did_means$post == 0]
ctrl_post <- did_means$mean_perf[did_means$treat == 0 & did_means$post == 1]
DiD_manual <- (treat_post - treat_pre) - (ctrl_post - ctrl_pre)
print(DiD_manual)
## [1] 0.3270175
and plot it afterwards
did_means_plot <- did_means %>%
mutate(
group_label = ifelse(treat == 1,
"WFH due to building issues",
"Remain working on-site")
)
ggplot(did_means_plot,
aes(x = post, y = mean_perf,
color = group_label, group = group_label)) +
geom_line(size = 1) +
geom_point(size = 3) +
scale_x_continuous(breaks = c(0, 1),
labels = c("Pre", "Post")) +
labs(
title = "Difference-in-Differences: Performance by Work Location",
x = "Period",
y = "Average performance (perform1)",
color = "Group"
) +
theme_minimal()
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Finally, we run the regression to look at our results
did_reg0 <- lm(
perform1 ~ treat * post,
data = panel_balanced
)
stargazer(did_reg0,
type = "text",
title = "DiD Regression without Controls",
dep.var.labels = "Performance (perform1)",
covariate.labels = c("Treatment (treat)",
"Post Period (post)",
"Treatment x Post Interaction"),
star.cutoffs = c(0.05, 0.01, 0.001),
notes = "Standard errors")
##
## DiD Regression without Controls
## ==========================================================
## Dependent variable:
## -----------------------------
## Performance (perform1)
## ----------------------------------------------------------
## Treatment (treat) 0.0001
## (0.030)
##
## Post Period (post) -0.155***
## (0.037)
##
## Treatment x Post Interaction 0.327***
## (0.050)
##
## Constant -0.002
## (0.021)
##
## ----------------------------------------------------------
## Observations 7,220
## R2 0.010
## Adjusted R2 0.009
## Residual Std. Error 1.011 (df = 7216)
## F Statistic 23.557*** (df = 3; 7216)
## ==========================================================
## Note: *p<0.05; **p<0.01; ***p<0.001
## Standard errors
The DiD results without controls suggest a clear positive performance
effect of forced WFH due to building issues. The treatment-group
baseline difference in the pre-period is essentially zero and not
significant (Treatment = 0.0001, SE =
0.030), indicating that treated and non-treated employees had
comparable performance before the disruption. After the
building issues began, performance in the control group declined
significantly (Post = -0.155, SE =
0.037, p < 0.001).
Crucially, the Treatment × Post interaction is
positive and highly significant
(0.327, SE = 0.050, p <
0.001). This is the DiD estimate and implies that employees who
were forced to work remotely experienced a 0.327-unit higher
change in performance relative to those who
remained on-site after the building issues started. Put differently,
while the on-site group shows a drop of −0.155, the
treated group’s implied change is −0.155 + 0.327 =
+0.172, suggesting a net improvement in
performance for the forced-WFH group during the post
period.
Overall, this baseline model (N = 7,220) indicates that the building-issues-driven shift to remote work is associated with a statistically significant relative increase in performance, even though the overall explanatory power is modest (R² = 0.010), which is typical for parsimonious DiD specifications.
After completing the exploratory data analysis and gaining an initial understanding of weekly employee outcomes and group differences, the next step is to estimate panel data models to quantify the impact of the building-issue-induced shift to working from home. The dataset final_panel_weekly contains repeated weekly observations for employees (personid) over time (year_week), which allows us to control for unobserved, time-invariant individual heterogeneity and common time shocks.
Given the building issues that started on 12.12.2022, we implement a Difference-in-Differences (DiD) framework in a panel setting by constructing a treatment structure that distinguishes between (i) employees who were ever affected by the event and (ii) the post-event period. The key treatment variable is defined as the interaction of these two components (wfh_post = treated × post). This setup enables the estimation of causal effects under the standard parallel trends assumption.
Following the course approach, we estimate and compare multiple specifications: Pooled OLS, First Differences (FD), and Fixed Effects (FE) with time dummies. Since the panel is not fully balanced, the FE estimator still remains appropriate and uses all available information. To address potential serial correlation in weekly panel errors, we complement the baseline results with robust/cluster-robust standard errors.
The following section presents a short descriptive summary followed by the panel regression specifications and R code in the exact sequence used during the analysis.
Defined the panel structure using employee IDs (personid) and weekly time identifiers (year_week).
Constructed the DiD design by creating: treated: indicator for employees ever affected by the building issue, post: indicator for weeks after 12.12.2022, wfh_post = treated × post: main DiD treatment variable.
Checked the panel balance and documented that the dataset is unbalanced
Assessed the parallel trends assumption using descriptive group comparisons over time.
Estimated baseline pooled models with weekly time dummies to establish initial effect directions.
Estimated FD models as an alternative way to remove time-invariant individual heterogeneity.
Estimated two-way FE models with individual and weekly fixed effects as the preferred specification.
Compared coefficient stability across Pooled OLS, FD, and FE models to assess robustness.
Tested for serial correlation in the FE residuals using the Breusch–Godfrey/Wooldridge test.
Reported robust inference by using heteroskedasticity- and serial-correlation-consistent standard errors.
# creating a tibble to work with
tb <- as_tibble(final_panel_weekly)
# last two weeks have no performance data so we remove them from our regression
tb <- tb %>%
filter(!(year == 2023 & week %in% c(34, 35)))
we did a quick data type check to ensure data types are in the correct formats for our regression
if ("date" %in% names(tb)) {
tb <- tb %>%
mutate(date = as.Date(date))
}
tb <- tb %>%
mutate(
year = as.integer(year),
week = as.integer(week)
)
# creating time identifier
tb <- tb %>%
mutate(
year_week = paste0(year, "-", sprintf("%02d", week))
)
Next, we also check the WFH indicator robustly and consistently,
ensuring that WFH_due_building_issues is stored as a
numeric 0/1 variable.
# checking the WFH indicator
if ("WFH_due_building_issue" %in% names(tb)) {
# If already exists, coerce safely to integer 0/1
tb <- tb %>%
mutate(
WFH_due_building_issue = as.integer(as.character(WFH_due_building_issue))
)
} else if ("WFH_due_building_issues" %in% names(tb)) {
# If plural version exists, possibly character like "Remote"
tb <- tb %>%
mutate(
WFH_due_building_issue = case_when(
is.numeric(WFH_due_building_issues) ~ as.integer(WFH_due_building_issues),
as.character(WFH_due_building_issues) %in% c("Remote", "WFH", "Yes", "1") ~ 1L,
TRUE ~ 0L
)
)
} else {
# Fallback to avoid breaking the code
tb <- tb %>% mutate(WFH_due_building_issue = 0L)
}
# Replace NA with 0 to be safe
tb <- tb %>%
mutate(WFH_due_building_issue = replace_na(WFH_due_building_issue, 0L))
after that we start to define our post period which starts from the date of building issues, in our case the 12th of December 2022.
# defining post period with the date but in case there is the problem with the week number as backup
if ("date" %in% names(tb)) {
tb <- tb %>%
mutate(post = as.integer(date >= as.Date("2022-12-12")))
} else {
tb <- tb %>%
mutate(post = as.integer(year > 2022 | (year == 2022 & week >= 50)))
}
# Replace NA with 0
tb <- tb %>% mutate(post = replace_na(post, 0L))
Next, we create the “treated” indicator at the person level and the standard DiD treatment variable. In theory this should equal our already existing indicator (WFH_due_building_issue) but we include this step to be sure and safe.
# treated indicator at person level
tb <- tb %>%
group_by(personid) %>%
mutate(treated = max(WFH_due_building_issue, na.rm = TRUE)) %>%
ungroup()
# standard DiD treatment variable: treated * post
tb <- tb %>%
mutate(wfh_post = treated * post)
summary statistics to check that everything is in numeric
sum_df <- tb %>%
transmute(
perform1 = as.numeric(perform1),
phonecall = as.numeric(phonecall),
phonecallraw = as.numeric(phonecallraw),
logphonecall = if ("logphonecall" %in% names(tb)) as.numeric(logphonecall) else NA_real_,
WFH_due_building_issue = as.numeric(WFH_due_building_issue),
treated = as.numeric(treated),
post = as.numeric(post),
wfh_post = as.numeric(wfh_post)
)
stargazer(
as.data.frame(sum_df),
median = TRUE,
type = "text",
header = FALSE, font.size = "small",
title = "Summary Statistics"
)
##
## Summary Statistics
## =================================================================
## Statistic N Mean St. Dev. Min Median Max
## -----------------------------------------------------------------
## perform1 9,847 -0.016 0.987 -3.031 0.046 4.163
## phonecall 9,736 -0.008 0.964 -3.113 0.066 5.828
## phonecallraw 9,589 440.214 142.528 1 445 1,264
## logphonecall 9,589 6.010 0.481 0.000 6.098 7.142
## WFH_due_building_issue 9,957 0.195 0.396 0 0 1
## treated 9,957 0.529 0.499 0 1 1
## post 9,957 0.397 0.489 0 0 1
## wfh_post 9,957 0.221 0.415 0 0 1
## -----------------------------------------------------------------
in the next step we check for panel balance.
# Check for panel balance
tb %>%
dplyr::select(year_week, personid) %>%
table()
## personid
## year_week 4122 6278 7720 8834 8854 10098 10356 12426 12974 13980 14048 14220
## 2022-01 1 1 1 1 0 1 1 0 1 1 1 0
## 2022-02 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-03 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-04 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-05 1 1 1 1 0 1 1 1 1 1 1 1
## 2022-06 1 1 1 1 0 1 1 1 1 1 1 1
## 2022-07 1 1 1 1 0 1 1 1 1 1 1 1
## 2022-08 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-09 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-10 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-11 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-12 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-13 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-14 1 1 0 1 1 1 1 1 1 1 1 1
## 2022-15 1 0 0 1 1 1 1 0 1 1 1 1
## 2022-16 1 0 0 1 1 1 1 0 1 1 1 1
## 2022-17 1 0 0 1 1 1 1 0 1 1 1 1
## 2022-18 1 1 0 1 1 1 1 0 1 1 1 1
## 2022-19 1 1 1 1 1 1 1 0 1 1 1 1
## 2022-20 1 1 1 1 1 1 1 0 1 1 1 1
## 2022-21 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-22 1 0 0 1 1 1 1 0 1 1 1 1
## 2022-23 1 0 0 1 1 1 1 0 1 1 1 1
## 2022-24 1 0 0 1 1 1 1 0 1 1 1 1
## 2022-25 1 0 0 1 1 1 1 0 1 1 1 1
## 2022-26 1 0 0 1 1 1 1 0 1 1 1 1
## 2022-27 1 0 0 1 1 1 1 0 1 1 1 1
## 2022-28 1 0 0 1 1 1 1 0 1 1 1 1
## 2022-29 1 0 0 1 1 1 1 0 1 1 1 1
## 2022-30 1 0 0 1 1 1 1 0 1 1 1 1
## 2022-31 1 0 0 1 1 1 1 0 1 1 1 1
## 2022-32 1 0 0 1 1 1 1 0 1 1 1 1
## 2022-33 1 0 0 1 1 1 1 0 1 1 1 1
## 2022-34 1 1 1 1 1 1 1 0 1 1 1 1
## 2022-35 1 1 1 1 1 1 1 0 1 1 1 1
## 2022-36 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-37 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-38 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-39 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-40 1 1 1 1 1 1 1 1 1 1 0 1
## 2022-41 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-42 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-43 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-44 1 1 1 1 1 1 1 1 1 1 0 1
## 2022-45 1 1 1 1 1 1 1 1 1 1 1 1
## 2022-46 1 0 1 1 1 1 1 1 1 1 1 1
## 2022-47 1 0 1 1 1 1 1 1 1 1 1 1
## 2022-48 1 1 1 1 1 1 1 0 1 1 1 1
## 2022-49 1 1 1 1 1 1 1 0 1 1 1 1
## 2022-50 1 1 1 1 1 0 1 0 1 1 1 1
## 2022-51 1 1 1 1 1 0 1 0 1 1 1 1
## 2022-52 1 1 1 1 1 1 1 0 1 1 1 1
## 2022-53 1 1 1 1 1 1 1 0 1 1 1 1
## 2023-01 1 1 1 1 1 0 1 0 1 1 1 1
## 2023-02 1 1 1 1 1 0 1 0 1 1 1 1
## 2023-03 1 1 1 1 1 0 1 0 1 1 1 1
## 2023-04 1 1 1 1 1 0 1 0 1 1 1 1
## 2023-05 1 1 1 1 1 0 1 0 1 1 1 1
## 2023-06 1 1 1 1 1 0 1 0 1 1 1 1
## 2023-07 1 1 1 1 1 0 1 0 1 1 1 1
## 2023-08 1 1 1 1 1 0 1 0 1 1 1 1
## 2023-09 1 1 1 1 1 0 1 0 1 1 1 1
## 2023-10 1 1 1 1 1 0 1 0 1 1 1 1
## 2023-11 1 1 1 1 1 0 1 0 1 1 1 1
## 2023-12 1 1 1 1 1 0 1 0 1 1 1 1
## 2023-13 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-14 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-15 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-16 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-17 1 1 1 1 1 0 1 1 1 1 1 0
## 2023-18 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-19 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-20 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-21 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-22 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-23 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-24 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-25 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-26 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-27 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-28 1 0 1 1 1 0 1 0 1 1 1 0
## 2023-29 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-30 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-31 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-32 1 1 1 1 1 0 1 0 1 1 1 0
## 2023-33 1 1 1 1 1 0 1 0 1 1 1 0
## personid
## year_week 14522 14528 15444 16334 16422 16424 16514 16594 16596 17160 17906
## 2022-01 1 0 1 0 1 1 1 0 1 0 0
## 2022-02 1 1 1 1 1 1 1 1 1 1 1
## 2022-03 1 1 1 1 1 1 1 1 1 1 1
## 2022-04 1 1 1 1 1 1 1 1 1 1 1
## 2022-05 1 1 1 1 1 1 1 1 1 1 1
## 2022-06 1 1 1 1 1 1 1 1 1 1 1
## 2022-07 1 1 1 1 1 1 1 1 1 1 1
## 2022-08 1 0 1 1 1 1 1 1 1 1 1
## 2022-09 1 1 1 1 1 1 1 1 1 1 1
## 2022-10 1 1 1 1 1 1 1 1 1 1 1
## 2022-11 1 1 1 1 1 1 1 1 1 1 1
## 2022-12 1 1 1 1 1 1 1 1 1 1 1
## 2022-13 1 1 1 1 1 1 1 1 1 1 1
## 2022-14 1 1 1 1 0 1 1 0 1 0 0
## 2022-15 1 1 1 1 0 1 1 0 1 1 0
## 2022-16 1 1 1 1 0 1 1 0 1 1 0
## 2022-17 1 1 1 1 0 1 1 0 1 1 0
## 2022-18 1 1 1 1 0 1 1 0 1 1 0
## 2022-19 1 1 1 1 0 1 1 1 1 1 1
## 2022-20 1 1 1 1 0 1 1 1 1 1 1
## 2022-21 1 1 1 1 0 1 1 1 1 1 1
## 2022-22 1 0 0 1 0 1 1 1 1 0 0
## 2022-23 1 0 0 1 0 1 1 1 1 1 0
## 2022-24 1 0 0 1 0 1 1 1 1 0 0
## 2022-25 1 0 0 1 0 1 1 1 1 0 0
## 2022-26 1 0 0 1 1 1 1 1 1 0 0
## 2022-27 1 0 0 1 1 1 1 1 1 0 0
## 2022-28 1 0 0 1 1 1 1 1 1 0 0
## 2022-29 1 0 0 1 1 1 1 1 1 0 0
## 2022-30 1 0 0 1 1 1 1 1 1 0 1
## 2022-31 1 0 0 1 1 1 1 1 1 0 1
## 2022-32 1 0 0 1 1 1 1 1 1 0 1
## 2022-33 1 0 0 1 0 1 1 1 1 0 1
## 2022-34 1 0 1 1 0 1 1 1 1 1 1
## 2022-35 1 1 1 1 1 1 1 1 1 1 1
## 2022-36 1 1 1 1 1 1 1 0 1 1 0
## 2022-37 1 1 1 1 1 1 1 1 1 1 0
## 2022-38 1 1 1 1 1 1 1 1 1 1 0
## 2022-39 1 1 1 1 1 1 1 1 1 1 0
## 2022-40 1 1 1 1 1 1 1 1 1 1 0
## 2022-41 0 1 1 1 1 1 1 1 1 1 0
## 2022-42 0 1 0 1 1 1 1 1 1 1 1
## 2022-43 1 1 1 1 1 1 1 1 1 1 1
## 2022-44 1 1 1 1 1 1 1 1 1 1 1
## 2022-45 1 1 1 1 1 1 0 1 1 1 0
## 2022-46 1 1 1 1 1 0 1 1 1 1 1
## 2022-47 1 1 1 1 1 1 1 1 1 1 1
## 2022-48 0 1 1 1 1 1 1 1 1 1 1
## 2022-49 1 1 1 1 1 1 1 0 1 1 1
## 2022-50 1 1 1 1 1 1 1 1 1 1 1
## 2022-51 1 1 1 1 1 1 1 1 1 1 1
## 2022-52 1 1 1 1 1 1 1 0 1 1 1
## 2022-53 1 1 1 1 1 1 1 1 1 1 1
## 2023-01 1 1 1 0 0 1 1 1 1 1 1
## 2023-02 1 1 1 0 0 1 1 1 1 1 1
## 2023-03 1 0 1 0 0 1 1 1 1 1 1
## 2023-04 1 1 1 0 0 1 1 0 1 1 0
## 2023-05 1 1 1 0 0 1 1 0 1 1 1
## 2023-06 1 1 1 0 0 1 1 1 1 1 1
## 2023-07 1 1 0 0 0 1 1 1 1 1 1
## 2023-08 1 1 1 0 0 1 1 1 1 1 0
## 2023-09 1 1 1 0 0 1 1 1 1 0 1
## 2023-10 1 1 1 0 0 1 1 1 1 1 1
## 2023-11 1 1 1 0 0 1 1 0 1 1 1
## 2023-12 1 1 1 0 0 1 1 1 1 1 1
## 2023-13 1 1 1 0 0 1 1 1 1 1 0
## 2023-14 1 1 1 0 0 1 1 1 1 1 0
## 2023-15 1 1 1 0 0 1 1 1 1 1 0
## 2023-16 1 1 1 0 0 1 1 1 1 1 0
## 2023-17 1 1 1 0 0 1 1 0 1 1 0
## 2023-18 1 1 1 0 0 1 1 0 1 1 0
## 2023-19 1 1 1 0 0 1 1 0 1 1 0
## 2023-20 1 1 1 0 0 1 1 0 1 1 0
## 2023-21 1 1 1 0 0 1 1 0 1 1 0
## 2023-22 1 1 1 0 0 1 1 0 1 1 0
## 2023-23 1 1 1 0 0 1 1 0 1 1 0
## 2023-24 1 1 1 0 0 1 1 0 1 1 0
## 2023-25 1 1 1 0 0 1 1 0 1 1 0
## 2023-26 1 1 1 0 0 1 1 0 1 1 0
## 2023-27 1 1 1 0 0 1 1 0 1 1 0
## 2023-28 1 1 1 0 0 1 1 0 1 1 0
## 2023-29 1 1 1 0 0 1 1 0 1 1 0
## 2023-30 1 1 1 0 0 1 1 0 1 1 0
## 2023-31 1 1 1 0 0 1 1 0 1 1 0
## 2023-32 1 1 1 0 0 1 1 0 1 1 0
## 2023-33 1 1 1 0 0 1 1 0 1 1 0
## personid
## year_week 19470 21654 22284 23136 23228 23772 24324 24608 25520 25638 25864
## 2022-01 1 0 1 1 0 1 1 1 1 1 1
## 2022-02 1 1 1 1 1 1 1 1 1 1 1
## 2022-03 1 1 1 1 1 1 1 0 1 1 1
## 2022-04 1 1 1 1 1 1 1 1 1 1 1
## 2022-05 1 0 1 1 1 1 1 1 1 1 1
## 2022-06 1 0 1 1 1 1 1 1 1 1 1
## 2022-07 1 0 1 1 1 1 1 1 1 1 1
## 2022-08 1 1 1 1 1 1 1 1 1 1 1
## 2022-09 1 1 1 1 1 1 1 1 1 1 1
## 2022-10 1 1 1 1 1 1 1 1 1 1 1
## 2022-11 1 1 1 1 1 1 1 1 1 1 1
## 2022-12 1 1 1 1 1 1 1 1 1 1 1
## 2022-13 1 1 1 1 1 1 1 1 1 1 1
## 2022-14 1 1 1 1 0 1 1 1 1 1 0
## 2022-15 1 1 1 1 0 1 1 1 1 1 0
## 2022-16 1 1 1 1 0 1 1 1 1 1 0
## 2022-17 1 1 1 1 0 1 1 1 1 1 0
## 2022-18 1 1 1 1 0 1 1 1 1 1 0
## 2022-19 1 1 1 1 1 1 1 1 1 1 1
## 2022-20 1 1 1 1 1 1 1 1 1 1 1
## 2022-21 1 1 1 1 1 1 1 1 1 1 1
## 2022-22 1 1 1 1 1 1 1 1 1 1 0
## 2022-23 1 1 1 1 0 1 1 1 1 1 0
## 2022-24 1 1 1 1 0 1 1 1 1 1 0
## 2022-25 1 1 1 1 0 1 1 1 1 1 0
## 2022-26 1 1 1 1 0 1 1 1 1 1 0
## 2022-27 1 1 1 1 1 1 1 1 1 1 0
## 2022-28 1 1 1 1 0 0 1 1 1 1 0
## 2022-29 1 1 1 1 0 0 1 1 1 1 0
## 2022-30 1 1 1 1 1 1 1 1 1 1 1
## 2022-31 1 1 0 1 1 1 1 1 1 1 1
## 2022-32 1 1 1 1 1 1 1 1 1 1 1
## 2022-33 1 1 1 1 1 1 1 1 1 1 1
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## 2022-47 1 1 1 1 1 1 1 1 1 1 1
## 2022-48 1 1 1 1 1 1 1 1 1 1 1
## 2022-49 1 1 1 1 1 1 1 1 1 1 1
## 2022-50 1 1 1 1 1 1 1 1 1 1 1
## 2022-51 1 1 1 1 1 1 1 1 1 1 1
## 2022-52 1 1 1 1 1 1 1 1 1 1 1
## 2022-53 1 1 1 1 1 1 1 1 1 0 1
## 2023-01 1 1 1 1 1 1 1 0 1 1 1
## 2023-02 1 1 1 1 1 1 1 1 1 1 1
## 2023-03 1 1 1 1 1 1 1 1 1 1 1
## 2023-04 1 1 1 1 1 1 1 1 1 1 1
## 2023-05 1 1 1 1 1 1 1 0 1 1 1
## 2023-06 1 1 1 1 1 1 1 1 1 1 1
## 2023-07 1 1 1 1 1 1 1 0 1 1 1
## 2023-08 1 1 1 1 1 1 1 0 1 1 1
## 2023-09 1 1 1 1 1 1 1 0 1 1 1
## 2023-10 1 1 1 1 1 1 1 0 1 1 1
## 2023-11 1 1 1 1 1 1 1 0 1 1 1
## 2023-12 1 1 1 1 1 1 1 0 1 1 1
## 2023-13 1 1 1 1 1 1 1 0 1 1 1
## 2023-14 1 1 1 1 1 1 1 0 1 1 1
## 2023-15 1 1 1 1 1 1 1 0 1 1 1
## 2023-16 1 1 1 1 1 1 1 0 1 1 1
## 2023-17 1 1 1 1 1 1 1 0 1 0 1
## 2023-18 1 1 1 1 1 1 1 0 1 1 1
## 2023-19 1 1 1 1 1 1 1 0 1 1 1
## 2023-20 1 1 1 1 1 1 1 0 1 1 1
## 2023-21 1 1 1 1 1 1 1 0 1 1 1
## 2023-22 1 1 1 1 1 1 1 0 1 1 1
## 2023-23 1 1 1 1 1 1 1 0 1 1 1
## 2023-24 1 1 1 1 1 1 1 0 1 1 1
## 2023-25 1 1 1 1 1 1 1 0 1 1 1
## 2023-26 1 1 1 1 1 1 1 0 1 1 1
## 2023-27 1 1 1 1 1 1 1 0 1 1 1
## 2023-28 1 1 1 1 1 1 1 0 1 0 1
## 2023-29 1 1 1 1 1 1 1 0 1 0 1
## 2023-30 1 1 1 1 1 1 1 0 1 1 1
## 2023-31 1 1 1 1 1 1 1 0 1 0 1
## 2023-32 1 1 1 1 1 1 1 0 1 0 1
## 2023-33 1 1 1 1 1 1 1 0 1 0 1
## personid
## year_week 40008 40034 40062 40162 40174 40192 40316 40322 40328 40336 40346
## 2022-01 1 1 1 1 1 1 1 1 1 1 1
## 2022-02 1 1 1 1 1 1 1 1 1 1 1
## 2022-03 1 1 1 1 1 1 1 1 1 1 1
## 2022-04 1 1 1 1 1 1 1 1 1 1 1
## 2022-05 1 1 1 1 1 1 1 1 1 1 1
## 2022-06 1 1 1 1 1 1 1 1 1 1 1
## 2022-07 1 1 1 1 1 1 1 1 1 1 1
## 2022-08 1 1 1 1 1 1 1 1 1 1 1
## 2022-09 1 1 1 1 1 1 1 1 1 1 1
## 2022-10 1 1 1 1 1 1 1 1 1 1 1
## 2022-11 1 1 1 1 1 1 1 1 1 1 1
## 2022-12 1 1 1 1 1 1 1 1 1 1 1
## 2022-13 1 1 1 1 1 1 1 1 1 1 1
## 2022-14 1 1 1 1 1 1 1 1 1 1 1
## 2022-15 1 1 1 1 1 1 1 1 1 1 1
## 2022-16 1 1 1 1 1 1 1 1 1 1 1
## 2022-17 1 1 1 1 1 1 1 1 1 1 1
## 2022-18 1 1 1 1 1 1 1 1 1 1 1
## 2022-19 1 1 1 1 1 1 1 1 1 1 1
## 2022-20 1 1 1 1 1 1 1 1 1 1 1
## 2022-21 1 1 1 1 1 1 1 1 1 0 1
## 2022-22 1 1 1 1 1 1 1 1 1 1 1
## 2022-23 1 1 1 1 1 1 1 1 1 1 1
## 2022-24 1 1 1 1 1 1 1 1 1 1 1
## 2022-25 1 1 1 1 1 1 1 1 1 1 1
## 2022-26 1 1 1 1 1 1 1 1 1 1 1
## 2022-27 1 1 1 1 1 1 1 1 1 1 1
## 2022-28 1 1 1 1 1 1 1 1 1 1 1
## 2022-29 1 1 1 1 1 1 1 1 1 0 1
## 2022-30 1 1 1 1 1 1 1 1 1 1 1
## 2022-31 1 1 1 1 1 1 1 1 1 1 1
## 2022-32 1 1 1 1 1 1 1 1 1 1 1
## 2022-33 1 1 1 1 1 1 1 1 1 1 1
## 2022-34 1 1 1 1 1 1 1 1 1 1 1
## 2022-35 1 1 1 1 1 1 1 1 1 1 1
## 2022-36 1 1 1 1 1 1 1 1 1 1 1
## 2022-37 1 1 1 1 1 1 1 1 1 0 1
## 2022-38 1 1 1 1 1 1 1 1 1 0 0
## 2022-39 1 1 1 1 1 1 1 1 1 1 1
## 2022-40 1 1 1 0 1 1 1 1 1 1 1
## 2022-41 1 1 1 1 1 1 1 1 1 1 1
## 2022-42 1 1 1 1 1 1 1 1 1 0 1
## 2022-43 1 1 1 1 1 1 1 1 1 0 0
## 2022-44 1 1 1 1 1 1 1 0 1 1 1
## 2022-45 1 1 1 1 1 1 1 0 1 1 1
## 2022-46 1 1 1 1 1 1 0 1 1 0 1
## 2022-47 1 1 1 1 1 1 1 1 1 0 1
## 2022-48 1 1 1 1 1 1 1 1 1 0 1
## 2022-49 1 1 1 1 1 1 1 1 1 1 1
## 2022-50 1 0 1 1 1 1 1 1 1 0 1
## 2022-51 1 1 1 1 1 1 1 1 1 0 1
## 2022-52 1 1 1 1 1 1 1 1 1 1 1
## 2022-53 1 1 1 1 0 1 1 1 1 0 1
## 2023-01 1 1 1 1 0 0 1 1 1 0 1
## 2023-02 1 1 1 1 1 1 1 1 1 0 1
## 2023-03 1 1 1 0 1 1 1 1 1 0 1
## 2023-04 1 1 1 0 1 0 1 1 1 1 1
## 2023-05 1 1 1 1 1 1 1 1 1 1 1
## 2023-06 1 1 1 0 1 1 1 1 1 0 1
## 2023-07 1 1 1 0 0 1 1 1 1 1 1
## 2023-08 1 1 0 1 0 1 1 1 1 1 1
## 2023-09 1 1 0 1 0 1 1 1 1 1 0
## 2023-10 1 1 0 1 0 1 1 1 1 1 0
## 2023-11 1 0 0 0 0 1 1 1 1 0 0
## 2023-12 1 1 0 1 0 1 1 1 1 0 0
## 2023-13 1 0 0 1 0 1 1 1 1 0 0
## 2023-14 1 0 0 1 0 1 1 1 1 0 0
## 2023-15 1 0 0 1 0 1 1 1 1 1 0
## 2023-16 1 0 0 1 0 1 1 1 1 1 0
## 2023-17 1 0 0 1 0 1 1 1 1 1 0
## 2023-18 1 0 0 1 0 1 1 1 1 1 0
## 2023-19 1 0 0 1 0 1 1 1 1 1 0
## 2023-20 1 0 0 1 0 1 1 1 1 0 0
## 2023-21 1 0 0 1 0 1 1 1 1 1 0
## 2023-22 1 0 0 1 0 1 1 1 1 0 0
## 2023-23 1 0 0 1 0 1 1 1 1 0 0
## 2023-24 0 0 0 1 0 1 1 1 1 0 0
## 2023-25 0 0 0 1 0 1 1 1 1 1 0
## 2023-26 1 0 0 1 0 1 1 1 1 1 0
## 2023-27 0 0 0 1 0 1 1 1 1 1 0
## 2023-28 0 0 0 1 0 1 1 1 1 1 0
## 2023-29 0 0 0 1 0 1 1 1 1 1 0
## 2023-30 0 0 0 1 0 1 1 1 1 1 0
## 2023-31 0 0 0 1 0 1 1 1 1 1 0
## 2023-32 0 0 0 1 0 0 1 1 1 1 0
## 2023-33 0 0 0 1 0 0 1 1 1 1 0
## personid
## year_week 40456 40472 40490 41286 41320 41332 42096 42104 42108 42152 42308
## 2022-01 1 1 1 1 1 1 0 0 1 0 0
## 2022-02 1 1 1 1 1 1 1 1 1 1 1
## 2022-03 1 1 1 1 1 1 1 1 1 1 1
## 2022-04 1 1 1 1 1 1 1 1 1 1 1
## 2022-05 1 1 1 1 1 1 1 1 1 1 1
## 2022-06 1 1 1 1 1 1 1 1 1 1 1
## 2022-07 1 1 1 1 1 1 1 1 1 1 1
## 2022-08 1 1 1 1 1 1 1 1 1 1 1
## 2022-09 1 1 1 1 1 1 1 1 1 1 1
## 2022-10 1 1 1 1 1 1 1 1 1 1 1
## 2022-11 1 1 1 1 1 1 1 1 1 1 1
## 2022-12 1 1 1 1 1 1 1 1 1 1 1
## 2022-13 1 1 1 1 1 1 1 1 1 1 1
## 2022-14 1 1 1 1 1 1 1 1 1 1 1
## 2022-15 1 1 1 1 1 1 1 1 1 1 1
## 2022-16 1 1 1 1 1 1 1 1 1 1 1
## 2022-17 1 1 1 1 1 1 1 1 1 1 1
## 2022-18 1 1 1 1 1 1 1 1 1 1 1
## 2022-19 1 1 1 1 1 1 1 1 1 1 1
## 2022-20 1 1 1 1 1 1 1 1 1 1 1
## 2022-21 1 1 1 1 1 1 1 1 1 1 1
## 2022-22 1 1 1 1 1 1 1 1 1 1 1
## 2022-23 1 1 1 1 1 1 1 1 1 1 1
## 2022-24 1 1 1 1 1 1 1 1 1 1 1
## 2022-25 1 1 1 1 1 1 1 1 1 1 1
## 2022-26 1 1 1 1 1 1 1 1 1 1 1
## 2022-27 1 1 1 1 1 1 1 1 1 1 1
## 2022-28 1 1 1 1 1 1 1 1 1 1 1
## 2022-29 1 1 1 1 1 1 1 1 1 1 1
## 2022-30 1 1 1 1 1 1 1 1 1 1 1
## 2022-31 1 1 1 1 1 1 1 1 1 1 1
## 2022-32 1 1 1 1 1 1 1 1 1 1 1
## 2022-33 1 1 1 1 1 1 1 1 1 1 1
## 2022-34 1 1 1 1 1 1 1 1 1 1 1
## 2022-35 1 1 1 1 1 1 1 1 1 1 1
## 2022-36 1 1 1 1 1 1 1 1 1 1 1
## 2022-37 1 1 1 1 1 1 1 1 1 1 1
## 2022-38 1 1 1 1 1 1 1 1 1 1 1
## 2022-39 1 1 1 1 1 1 0 1 0 1 1
## 2022-40 1 1 1 1 1 1 1 1 1 1 1
## 2022-41 1 1 1 1 1 1 1 1 1 1 1
## 2022-42 1 1 1 1 1 1 1 1 1 1 1
## 2022-43 1 1 1 1 1 1 1 1 1 1 1
## 2022-44 1 1 1 1 1 1 1 1 1 1 0
## 2022-45 1 1 1 1 0 1 1 1 1 1 1
## 2022-46 1 1 1 1 1 1 1 1 1 1 1
## 2022-47 1 1 1 1 1 1 1 1 1 1 1
## 2022-48 1 1 1 1 1 1 1 1 1 1 1
## 2022-49 1 1 1 1 1 1 1 1 1 1 1
## 2022-50 1 1 1 1 1 1 1 1 1 1 0
## 2022-51 1 1 1 1 1 1 0 1 1 1 0
## 2022-52 1 1 1 1 0 1 0 0 1 1 0
## 2022-53 1 1 1 1 1 0 0 1 1 1 1
## 2023-01 1 1 1 1 1 1 0 1 1 1 1
## 2023-02 1 1 1 1 0 1 0 1 1 1 1
## 2023-03 1 1 1 1 0 1 0 1 1 1 1
## 2023-04 1 1 1 1 0 0 0 1 1 1 1
## 2023-05 1 1 1 1 0 1 0 1 1 1 1
## 2023-06 1 1 1 1 0 0 0 1 1 1 1
## 2023-07 1 1 1 1 0 0 0 1 1 1 1
## 2023-08 1 1 1 1 0 0 0 1 1 1 0
## 2023-09 1 1 1 1 0 0 0 1 1 1 0
## 2023-10 1 1 1 1 0 0 0 1 1 1 0
## 2023-11 1 1 1 1 0 0 0 1 1 1 0
## 2023-12 1 1 1 1 0 0 0 0 1 1 0
## 2023-13 1 1 1 0 0 0 0 0 1 1 0
## 2023-14 1 1 1 0 0 0 0 0 1 1 0
## 2023-15 1 1 1 0 0 0 0 0 1 1 0
## 2023-16 1 1 1 0 0 0 0 0 1 1 0
## 2023-17 1 1 1 1 0 0 0 0 1 1 0
## 2023-18 1 1 1 1 0 0 0 0 1 1 0
## 2023-19 1 1 1 1 0 0 0 0 1 1 0
## 2023-20 1 1 1 1 0 0 0 0 1 1 0
## 2023-21 1 1 1 1 0 0 0 0 1 1 0
## 2023-22 1 1 1 1 0 0 0 0 1 1 0
## 2023-23 1 1 1 1 0 0 0 0 1 1 0
## 2023-24 1 1 1 1 0 0 0 0 1 1 0
## 2023-25 1 1 1 1 0 0 0 0 1 1 0
## 2023-26 1 1 1 1 0 0 0 0 1 1 0
## 2023-27 1 1 1 1 0 0 0 0 1 1 0
## 2023-28 1 1 1 1 0 0 0 0 1 1 0
## 2023-29 1 1 1 1 0 0 0 0 1 1 0
## 2023-30 1 1 1 1 0 0 0 0 1 1 0
## 2023-31 1 1 1 1 0 0 0 0 1 1 0
## 2023-32 1 1 1 1 0 0 0 0 1 1 0
## 2023-33 1 1 1 1 0 0 0 0 1 1 0
## personid
## year_week 42592 42618 42624 42628 42632 42634 42682 43258 43264 43288 43524
## 2022-01 0 0 0 0 0 0 0 0 0 0 0
## 2022-02 0 0 0 0 0 0 0 0 0 0 0
## 2022-03 0 0 0 0 0 0 0 0 0 0 0
## 2022-04 0 0 0 0 0 0 0 0 0 0 0
## 2022-05 1 1 1 1 1 1 1 0 0 0 0
## 2022-06 1 1 1 1 1 1 1 0 0 0 0
## 2022-07 1 1 1 1 1 1 1 0 0 0 0
## 2022-08 1 1 1 1 1 1 1 0 0 0 0
## 2022-09 1 1 1 1 1 1 1 0 0 0 0
## 2022-10 1 1 1 1 1 1 1 0 0 0 0
## 2022-11 1 1 1 1 1 1 1 0 0 0 0
## 2022-12 1 1 1 1 1 1 1 1 1 1 0
## 2022-13 1 1 1 1 1 1 1 1 1 1 0
## 2022-14 1 1 1 1 1 1 1 1 1 1 1
## 2022-15 1 1 1 1 1 1 1 1 1 1 1
## 2022-16 1 1 1 1 1 1 1 1 1 1 1
## 2022-17 1 1 1 1 1 1 1 1 1 1 1
## 2022-18 1 1 1 1 1 1 1 1 1 1 0
## 2022-19 1 1 1 1 1 1 1 1 1 1 1
## 2022-20 1 1 1 1 1 1 1 1 1 1 1
## 2022-21 1 1 1 1 1 1 1 1 1 1 1
## 2022-22 1 1 1 1 1 1 1 1 1 1 1
## 2022-23 1 1 1 1 1 1 1 1 1 1 1
## 2022-24 1 1 1 1 1 1 1 1 1 1 1
## 2022-25 1 1 1 1 1 1 1 1 1 1 1
## 2022-26 1 1 1 1 1 1 1 1 1 1 1
## 2022-27 1 1 1 1 1 1 1 1 1 1 1
## 2022-28 1 1 1 1 1 1 1 1 1 1 1
## 2022-29 1 1 1 1 1 0 1 1 1 1 1
## 2022-30 1 1 1 1 1 1 1 1 1 1 1
## 2022-31 1 1 1 1 1 1 1 1 1 1 1
## 2022-32 1 1 1 1 1 1 1 1 1 1 1
## 2022-33 1 1 1 1 1 1 1 1 1 1 1
## 2022-34 1 1 1 1 1 1 1 1 1 1 1
## 2022-35 1 1 1 1 1 1 1 1 1 1 1
## 2022-36 1 0 1 1 1 1 1 1 1 1 1
## 2022-37 1 0 1 1 1 1 1 1 0 0 1
## 2022-38 1 1 1 1 1 1 1 1 1 0 1
## 2022-39 1 1 1 1 1 1 1 0 1 0 1
## 2022-40 1 1 1 1 1 1 1 0 1 1 0
## 2022-41 1 1 1 1 1 1 0 1 1 1 0
## 2022-42 1 1 1 1 1 0 1 1 1 1 1
## 2022-43 1 1 1 1 1 0 1 1 1 1 1
## 2022-44 1 1 1 1 1 0 1 1 1 1 1
## 2022-45 1 1 1 0 1 0 1 1 1 1 1
## 2022-46 1 1 1 1 1 1 1 1 1 1 1
## 2022-47 1 1 1 1 1 1 1 1 1 1 1
## 2022-48 1 1 1 1 1 1 1 1 0 1 1
## 2022-49 1 1 1 1 1 1 1 1 0 1 1
## 2022-50 1 1 1 1 1 1 1 1 0 0 1
## 2022-51 1 1 1 1 1 1 0 1 1 0 1
## 2022-52 1 1 1 1 1 1 0 1 1 1 1
## 2022-53 1 0 1 1 1 1 0 1 1 1 0
## 2023-01 1 0 1 1 1 1 0 1 1 0 1
## 2023-02 1 0 1 1 1 1 0 1 1 0 1
## 2023-03 1 1 1 1 0 1 0 1 1 0 1
## 2023-04 1 1 1 1 1 1 0 1 1 1 1
## 2023-05 1 0 1 1 1 1 0 1 1 1 1
## 2023-06 1 0 1 1 1 1 0 1 1 1 1
## 2023-07 1 0 1 1 1 1 0 1 1 1 1
## 2023-08 1 0 1 1 1 1 0 1 1 1 1
## 2023-09 1 0 1 1 1 1 0 1 1 1 0
## 2023-10 1 0 1 1 1 1 0 1 1 1 0
## 2023-11 1 0 1 1 1 1 0 1 1 0 0
## 2023-12 1 0 1 1 1 1 0 1 1 1 0
## 2023-13 1 0 1 1 1 1 0 1 1 1 0
## 2023-14 1 0 1 1 1 0 0 1 1 0 0
## 2023-15 1 0 1 1 1 1 0 1 1 0 0
## 2023-16 1 0 1 1 1 0 0 1 1 0 0
## 2023-17 1 0 1 1 1 1 0 1 1 0 0
## 2023-18 1 0 1 1 1 1 0 1 1 0 0
## 2023-19 1 0 1 1 1 1 0 1 0 0 0
## 2023-20 1 0 1 1 1 0 0 1 1 0 0
## 2023-21 1 0 1 1 1 1 0 1 1 0 0
## 2023-22 1 0 1 1 1 1 0 1 1 0 0
## 2023-23 1 0 1 1 1 1 0 1 1 0 0
## 2023-24 1 0 1 1 1 1 0 1 1 0 0
## 2023-25 1 0 1 1 1 0 0 1 1 0 0
## 2023-26 1 0 1 1 1 1 0 1 1 0 0
## 2023-27 1 0 1 1 1 1 0 1 1 0 0
## 2023-28 1 0 1 1 1 1 0 1 1 0 0
## 2023-29 1 0 1 1 0 0 0 1 1 0 0
## 2023-30 1 0 1 1 1 1 0 1 1 0 0
## 2023-31 1 0 1 1 1 0 0 1 1 0 0
## 2023-32 1 0 1 1 0 0 0 1 1 0 0
## 2023-33 1 0 1 1 1 0 0 1 1 0 0
## personid
## year_week 43534 43570 43926 44256 44266 44282 44408 44782 44784 44794 44800
## 2022-01 0 0 0 0 0 0 0 0 0 0 0
## 2022-02 0 0 0 0 0 0 0 0 0 0 0
## 2022-03 0 0 0 0 0 0 0 0 0 0 0
## 2022-04 0 0 0 0 0 0 0 0 0 0 0
## 2022-05 0 0 0 0 0 0 0 0 0 0 0
## 2022-06 0 0 0 0 0 0 0 0 0 0 0
## 2022-07 0 0 0 0 0 0 0 0 0 0 0
## 2022-08 0 0 0 0 0 0 0 0 0 0 0
## 2022-09 0 0 0 0 0 0 0 0 0 0 0
## 2022-10 0 0 0 0 0 0 0 0 0 0 0
## 2022-11 0 0 0 0 0 0 0 0 0 0 0
## 2022-12 0 0 0 0 0 0 0 0 0 0 0
## 2022-13 0 0 0 0 0 0 0 0 0 0 0
## 2022-14 1 0 0 0 0 0 0 0 0 0 0
## 2022-15 1 1 0 0 0 0 0 0 0 0 0
## 2022-16 1 1 0 0 0 0 0 0 0 0 0
## 2022-17 1 1 1 0 0 0 0 0 0 0 0
## 2022-18 0 1 1 0 1 1 0 0 0 0 0
## 2022-19 1 1 1 1 1 1 1 0 0 0 0
## 2022-20 1 1 1 1 1 1 1 0 0 0 0
## 2022-21 1 1 1 1 1 1 1 0 0 0 0
## 2022-22 1 1 1 1 1 1 1 0 0 1 0
## 2022-23 1 1 1 1 1 1 1 1 1 1 0
## 2022-24 1 1 1 1 1 1 1 1 1 1 1
## 2022-25 1 1 1 1 1 1 1 1 1 1 1
## 2022-26 1 1 1 1 1 1 1 1 1 1 1
## 2022-27 1 1 1 1 1 1 1 1 1 1 1
## 2022-28 1 1 1 1 1 1 1 1 1 1 1
## 2022-29 1 1 1 1 1 1 1 1 1 1 1
## 2022-30 1 1 1 1 1 1 1 1 1 1 1
## 2022-31 1 1 1 1 1 1 1 1 1 1 1
## 2022-32 1 1 1 1 1 1 1 1 1 1 1
## 2022-33 1 1 1 1 1 1 1 1 1 1 1
## 2022-34 1 1 1 1 1 1 1 1 1 1 1
## 2022-35 1 1 1 1 1 1 1 1 1 1 1
## 2022-36 1 1 1 1 1 1 1 1 1 1 1
## 2022-37 1 1 1 1 1 1 1 1 1 1 1
## 2022-38 0 1 1 1 1 1 1 1 1 1 1
## 2022-39 0 1 1 1 0 1 1 1 1 1 1
## 2022-40 1 1 1 1 1 1 1 1 1 1 1
## 2022-41 1 1 1 1 1 1 1 1 1 1 1
## 2022-42 1 1 1 1 1 1 1 1 1 1 1
## 2022-43 1 1 1 1 1 1 1 1 1 1 1
## 2022-44 1 1 1 1 1 1 1 1 1 1 1
## 2022-45 1 1 1 1 1 1 1 1 1 1 1
## 2022-46 1 1 1 1 1 1 1 1 1 0 1
## 2022-47 1 1 1 1 1 1 1 1 1 1 1
## 2022-48 1 1 1 1 1 1 1 1 1 1 1
## 2022-49 1 1 1 0 1 1 1 1 1 1 1
## 2022-50 1 1 1 1 1 1 1 1 1 1 1
## 2022-51 1 1 1 0 1 1 1 1 1 1 1
## 2022-52 1 1 1 1 1 1 1 1 1 1 1
## 2022-53 1 1 1 1 1 1 1 1 1 1 1
## 2023-01 1 1 1 1 0 1 1 1 1 0 1
## 2023-02 0 1 1 1 1 1 1 1 1 0 1
## 2023-03 1 1 1 1 1 1 1 1 1 0 1
## 2023-04 1 1 1 0 1 1 1 1 1 0 1
## 2023-05 1 1 1 0 1 1 1 1 1 0 1
## 2023-06 1 1 1 0 1 1 1 1 1 0 1
## 2023-07 1 1 1 0 1 1 1 1 1 0 1
## 2023-08 1 1 1 0 1 1 1 1 1 0 1
## 2023-09 1 1 1 1 1 1 1 1 1 0 1
## 2023-10 1 1 1 0 1 1 1 1 1 0 1
## 2023-11 1 1 1 0 1 1 1 1 1 0 1
## 2023-12 1 1 1 1 1 1 1 1 1 0 1
## 2023-13 1 1 1 1 1 1 1 1 1 0 1
## 2023-14 1 1 1 1 1 1 1 1 1 0 1
## 2023-15 1 1 1 1 1 1 1 1 1 0 1
## 2023-16 1 1 1 1 1 1 1 1 1 0 1
## 2023-17 1 1 1 1 1 1 1 1 1 0 1
## 2023-18 1 1 1 1 1 1 1 1 1 0 1
## 2023-19 1 1 1 1 1 1 1 1 1 0 1
## 2023-20 1 1 1 1 1 1 1 1 1 0 1
## 2023-21 1 1 1 0 1 1 1 1 1 0 1
## 2023-22 1 1 1 0 1 1 1 1 1 0 1
## 2023-23 1 1 1 0 1 1 1 1 1 0 1
## 2023-24 1 1 1 0 1 1 1 1 1 0 1
## 2023-25 1 1 1 1 1 1 1 1 1 0 1
## 2023-26 1 1 1 0 1 1 1 1 1 0 1
## 2023-27 1 1 1 0 1 1 1 1 1 0 1
## 2023-28 1 1 1 0 1 1 1 1 1 0 1
## 2023-29 1 1 1 0 1 1 1 1 1 0 1
## 2023-30 0 1 1 0 1 1 1 1 1 0 1
## 2023-31 1 1 1 0 1 1 1 1 1 0 1
## 2023-32 1 1 1 0 1 1 1 1 1 0 1
## 2023-33 1 1 1 0 1 1 1 1 1 0 1
## personid
## year_week 45254 45442
## 2022-01 0 0
## 2022-02 0 0
## 2022-03 0 0
## 2022-04 0 0
## 2022-05 0 0
## 2022-06 0 0
## 2022-07 0 0
## 2022-08 0 0
## 2022-09 0 0
## 2022-10 0 0
## 2022-11 0 0
## 2022-12 0 0
## 2022-13 0 0
## 2022-14 0 0
## 2022-15 0 0
## 2022-16 0 0
## 2022-17 0 0
## 2022-18 0 0
## 2022-19 0 0
## 2022-20 0 0
## 2022-21 0 0
## 2022-22 0 0
## 2022-23 1 0
## 2022-24 1 0
## 2022-25 1 0
## 2022-26 1 1
## 2022-27 0 1
## 2022-28 1 1
## 2022-29 1 1
## 2022-30 1 1
## 2022-31 1 1
## 2022-32 1 1
## 2022-33 1 1
## 2022-34 1 1
## 2022-35 1 1
## 2022-36 1 1
## 2022-37 1 1
## 2022-38 1 1
## 2022-39 1 1
## 2022-40 1 1
## 2022-41 1 1
## 2022-42 1 1
## 2022-43 1 1
## 2022-44 1 1
## 2022-45 1 1
## 2022-46 1 1
## 2022-47 1 1
## 2022-48 1 1
## 2022-49 1 1
## 2022-50 1 1
## 2022-51 1 1
## 2022-52 1 1
## 2022-53 1 1
## 2023-01 1 1
## 2023-02 1 1
## 2023-03 1 1
## 2023-04 1 1
## 2023-05 1 1
## 2023-06 1 1
## 2023-07 1 1
## 2023-08 1 1
## 2023-09 1 1
## 2023-10 1 1
## 2023-11 1 1
## 2023-12 1 1
## 2023-13 1 1
## 2023-14 1 1
## 2023-15 1 1
## 2023-16 1 1
## 2023-17 1 1
## 2023-18 1 1
## 2023-19 1 1
## 2023-20 1 1
## 2023-21 1 1
## 2023-22 1 1
## 2023-23 1 1
## 2023-24 1 1
## 2023-25 1 1
## 2023-26 1 1
## 2023-27 1 1
## 2023-28 1 1
## 2023-29 1 1
## 2023-30 1 1
## 2023-31 1 1
## 2023-32 1 1
## 2023-33 1 1
is.pbalanced(tb, index = c("personid", "year_week"))
## [1] FALSE
In this case we get false which indicates an unbalanced panel. Next on, we check when the treatment occurred in terms of weeks and years.
# When did treatment occur?
tb %>%
dplyr::select(WFH_due_building_issue, year, week) %>%
table()
## , , week = 1
##
## year
## WFH_due_building_issue 2022 2023
## 0 94 59
## 1 0 59
##
## , , week = 2
##
## year
## WFH_due_building_issue 2022 2023
## 0 110 64
## 1 0 56
##
## , , week = 3
##
## year
## WFH_due_building_issue 2022 2023
## 0 109 63
## 1 0 57
##
## , , week = 4
##
## year
## WFH_due_building_issue 2022 2023
## 0 110 60
## 1 0 56
##
## , , week = 5
##
## year
## WFH_due_building_issue 2022 2023
## 0 114 61
## 1 0 57
##
## , , week = 6
##
## year
## WFH_due_building_issue 2022 2023
## 0 114 59
## 1 0 57
##
## , , week = 7
##
## year
## WFH_due_building_issue 2022 2023
## 0 113 60
## 1 0 55
##
## , , week = 8
##
## year
## WFH_due_building_issue 2022 2023
## 0 116 58
## 1 0 56
##
## , , week = 9
##
## year
## WFH_due_building_issue 2022 2023
## 0 116 57
## 1 0 56
##
## , , week = 10
##
## year
## WFH_due_building_issue 2022 2023
## 0 117 53
## 1 0 59
##
## , , week = 11
##
## year
## WFH_due_building_issue 2022 2023
## 0 118 50
## 1 0 57
##
## , , week = 12
##
## year
## WFH_due_building_issue 2022 2023
## 0 120 59
## 1 0 51
##
## , , week = 13
##
## year
## WFH_due_building_issue 2022 2023
## 0 121 55
## 1 0 52
##
## , , week = 14
##
## year
## WFH_due_building_issue 2022 2023
## 0 115 52
## 1 0 52
##
## , , week = 15
##
## year
## WFH_due_building_issue 2022 2023
## 0 115 52
## 1 0 53
##
## , , week = 16
##
## year
## WFH_due_building_issue 2022 2023
## 0 115 54
## 1 0 49
##
## , , week = 17
##
## year
## WFH_due_building_issue 2022 2023
## 0 116 58
## 1 0 45
##
## , , week = 18
##
## year
## WFH_due_building_issue 2022 2023
## 0 117 55
## 1 0 46
##
## , , week = 19
##
## year
## WFH_due_building_issue 2022 2023
## 0 126 51
## 1 0 50
##
## , , week = 20
##
## year
## WFH_due_building_issue 2022 2023
## 0 127 50
## 1 0 49
##
## , , week = 21
##
## year
## WFH_due_building_issue 2022 2023
## 0 127 50
## 1 0 50
##
## , , week = 22
##
## year
## WFH_due_building_issue 2022 2023
## 0 120 50
## 1 0 49
##
## , , week = 23
##
## year
## WFH_due_building_issue 2022 2023
## 0 122 55
## 1 0 44
##
## , , week = 24
##
## year
## WFH_due_building_issue 2022 2023
## 0 123 53
## 1 0 44
##
## , , week = 25
##
## year
## WFH_due_building_issue 2022 2023
## 0 123 54
## 1 0 43
##
## , , week = 26
##
## year
## WFH_due_building_issue 2022 2023
## 0 124 54
## 1 0 44
##
## , , week = 27
##
## year
## WFH_due_building_issue 2022 2023
## 0 125 53
## 1 0 44
##
## , , week = 28
##
## year
## WFH_due_building_issue 2022 2023
## 0 123 52
## 1 0 43
##
## , , week = 29
##
## year
## WFH_due_building_issue 2022 2023
## 0 123 50
## 1 0 43
##
## , , week = 30
##
## year
## WFH_due_building_issue 2022 2023
## 0 129 54
## 1 0 42
##
## , , week = 31
##
## year
## WFH_due_building_issue 2022 2023
## 0 127 51
## 1 0 44
##
## , , week = 32
##
## year
## WFH_due_building_issue 2022 2023
## 0 127 48
## 1 0 43
##
## , , week = 33
##
## year
## WFH_due_building_issue 2022 2023
## 0 127 48
## 1 0 44
##
## , , week = 34
##
## year
## WFH_due_building_issue 2022 2023
## 0 131 0
## 1 0 0
##
## , , week = 35
##
## year
## WFH_due_building_issue 2022 2023
## 0 134 0
## 1 0 0
##
## , , week = 36
##
## year
## WFH_due_building_issue 2022 2023
## 0 131 0
## 1 0 0
##
## , , week = 37
##
## year
## WFH_due_building_issue 2022 2023
## 0 128 0
## 1 0 0
##
## , , week = 38
##
## year
## WFH_due_building_issue 2022 2023
## 0 128 0
## 1 0 0
##
## , , week = 39
##
## year
## WFH_due_building_issue 2022 2023
## 0 126 0
## 1 0 0
##
## , , week = 40
##
## year
## WFH_due_building_issue 2022 2023
## 0 128 0
## 1 0 0
##
## , , week = 41
##
## year
## WFH_due_building_issue 2022 2023
## 0 131 0
## 1 0 0
##
## , , week = 42
##
## year
## WFH_due_building_issue 2022 2023
## 0 130 0
## 1 0 0
##
## , , week = 43
##
## year
## WFH_due_building_issue 2022 2023
## 0 130 0
## 1 0 0
##
## , , week = 44
##
## year
## WFH_due_building_issue 2022 2023
## 0 130 0
## 1 0 0
##
## , , week = 45
##
## year
## WFH_due_building_issue 2022 2023
## 0 128 0
## 1 0 0
##
## , , week = 46
##
## year
## WFH_due_building_issue 2022 2023
## 0 129 0
## 1 0 0
##
## , , week = 47
##
## year
## WFH_due_building_issue 2022 2023
## 0 133 0
## 1 0 0
##
## , , week = 48
##
## year
## WFH_due_building_issue 2022 2023
## 0 112 0
## 1 17 0
##
## , , week = 49
##
## year
## WFH_due_building_issue 2022 2023
## 0 81 0
## 1 50 0
##
## , , week = 50
##
## year
## WFH_due_building_issue 2022 2023
## 0 76 0
## 1 52 0
##
## , , week = 51
##
## year
## WFH_due_building_issue 2022 2023
## 0 69 0
## 1 58 0
##
## , , week = 52
##
## year
## WFH_due_building_issue 2022 2023
## 0 71 0
## 1 55 0
##
## , , week = 53
##
## year
## WFH_due_building_issue 2022 2023
## 0 68 0
## 1 57 0
tb %>%
dplyr::select(post, year, week) %>%
table()
## , , week = 1
##
## year
## post 2022 2023
## 0 94 0
## 1 0 118
##
## , , week = 2
##
## year
## post 2022 2023
## 0 110 0
## 1 0 120
##
## , , week = 3
##
## year
## post 2022 2023
## 0 109 0
## 1 0 120
##
## , , week = 4
##
## year
## post 2022 2023
## 0 110 0
## 1 0 116
##
## , , week = 5
##
## year
## post 2022 2023
## 0 114 0
## 1 0 118
##
## , , week = 6
##
## year
## post 2022 2023
## 0 114 0
## 1 0 116
##
## , , week = 7
##
## year
## post 2022 2023
## 0 113 0
## 1 0 115
##
## , , week = 8
##
## year
## post 2022 2023
## 0 116 0
## 1 0 114
##
## , , week = 9
##
## year
## post 2022 2023
## 0 116 0
## 1 0 113
##
## , , week = 10
##
## year
## post 2022 2023
## 0 117 0
## 1 0 112
##
## , , week = 11
##
## year
## post 2022 2023
## 0 118 0
## 1 0 107
##
## , , week = 12
##
## year
## post 2022 2023
## 0 120 0
## 1 0 110
##
## , , week = 13
##
## year
## post 2022 2023
## 0 121 0
## 1 0 107
##
## , , week = 14
##
## year
## post 2022 2023
## 0 115 0
## 1 0 104
##
## , , week = 15
##
## year
## post 2022 2023
## 0 115 0
## 1 0 105
##
## , , week = 16
##
## year
## post 2022 2023
## 0 115 0
## 1 0 103
##
## , , week = 17
##
## year
## post 2022 2023
## 0 116 0
## 1 0 103
##
## , , week = 18
##
## year
## post 2022 2023
## 0 117 0
## 1 0 101
##
## , , week = 19
##
## year
## post 2022 2023
## 0 126 0
## 1 0 101
##
## , , week = 20
##
## year
## post 2022 2023
## 0 127 0
## 1 0 99
##
## , , week = 21
##
## year
## post 2022 2023
## 0 127 0
## 1 0 100
##
## , , week = 22
##
## year
## post 2022 2023
## 0 120 0
## 1 0 99
##
## , , week = 23
##
## year
## post 2022 2023
## 0 122 0
## 1 0 99
##
## , , week = 24
##
## year
## post 2022 2023
## 0 123 0
## 1 0 97
##
## , , week = 25
##
## year
## post 2022 2023
## 0 123 0
## 1 0 97
##
## , , week = 26
##
## year
## post 2022 2023
## 0 124 0
## 1 0 98
##
## , , week = 27
##
## year
## post 2022 2023
## 0 125 0
## 1 0 97
##
## , , week = 28
##
## year
## post 2022 2023
## 0 123 0
## 1 0 95
##
## , , week = 29
##
## year
## post 2022 2023
## 0 123 0
## 1 0 93
##
## , , week = 30
##
## year
## post 2022 2023
## 0 129 0
## 1 0 96
##
## , , week = 31
##
## year
## post 2022 2023
## 0 127 0
## 1 0 95
##
## , , week = 32
##
## year
## post 2022 2023
## 0 127 0
## 1 0 91
##
## , , week = 33
##
## year
## post 2022 2023
## 0 127 0
## 1 0 92
##
## , , week = 34
##
## year
## post 2022 2023
## 0 131 0
## 1 0 0
##
## , , week = 35
##
## year
## post 2022 2023
## 0 134 0
## 1 0 0
##
## , , week = 36
##
## year
## post 2022 2023
## 0 131 0
## 1 0 0
##
## , , week = 37
##
## year
## post 2022 2023
## 0 128 0
## 1 0 0
##
## , , week = 38
##
## year
## post 2022 2023
## 0 128 0
## 1 0 0
##
## , , week = 39
##
## year
## post 2022 2023
## 0 126 0
## 1 0 0
##
## , , week = 40
##
## year
## post 2022 2023
## 0 128 0
## 1 0 0
##
## , , week = 41
##
## year
## post 2022 2023
## 0 131 0
## 1 0 0
##
## , , week = 42
##
## year
## post 2022 2023
## 0 130 0
## 1 0 0
##
## , , week = 43
##
## year
## post 2022 2023
## 0 130 0
## 1 0 0
##
## , , week = 44
##
## year
## post 2022 2023
## 0 130 0
## 1 0 0
##
## , , week = 45
##
## year
## post 2022 2023
## 0 128 0
## 1 0 0
##
## , , week = 46
##
## year
## post 2022 2023
## 0 129 0
## 1 0 0
##
## , , week = 47
##
## year
## post 2022 2023
## 0 133 0
## 1 0 0
##
## , , week = 48
##
## year
## post 2022 2023
## 0 129 0
## 1 0 0
##
## , , week = 49
##
## year
## post 2022 2023
## 0 131 0
## 1 0 0
##
## , , week = 50
##
## year
## post 2022 2023
## 0 0 0
## 1 128 0
##
## , , week = 51
##
## year
## post 2022 2023
## 0 0 0
## 1 127 0
##
## , , week = 52
##
## year
## post 2022 2023
## 0 0 0
## 1 126 0
##
## , , week = 53
##
## year
## post 2022 2023
## 0 0 0
## 1 125 0
Now we can have a brief look at the outcome distribution to see how performance looks like in general.
# Outcome distribution (main outcome = perform1)
ggplot(tb, aes(x = perform1)) +
geom_histogram() +
theme_bw() +
labs(x = "Overall performance score (z)", y = "Count")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 110 rows containing non-finite outside the scale range
## (`stat_bin()`).
Next, we check the parallel trends assumption by plotting performance over time as we have seen in lab8 for treated vs non-treated groups.
# Trend assumption (informal check)
# By year: treated vs not treated
ggplot(tb, aes(
x = factor(year),
y = perform1,
fill = factor(treated, levels = c(0, 1), labels = c("Not affected", "Affected"))
)) +
geom_boxplot() +
theme_bw() +
theme(legend.position = "bottom") +
scale_fill_tableau() +
labs(x = "Year", y = "Performance (z)", fill = "")
## Warning: Removed 110 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
# Weekly mean trend line (can be dense but informative)
ggplot(tb, aes(
x = year_week,
y = perform1,
color = factor(treated, levels = c(0, 1), labels = c("Not affected", "Affected"))
)) +
stat_summary(fun = mean, geom = "line", aes(group = factor(treated))) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5),
legend.position = "bottom") +
labs(x = "Year-Week", y = "Mean performance (z)", color = "")
## Warning: Removed 110 rows containing non-finite outside the scale range
## (`stat_summary()`).
Before the event, the average performance trends of the treated and
control groups appear broadly parallel, providing support for the
parallel trends assumption
Pooled OLS
# Pooled OLS (no person FE)
out.pl <- plm(
perform1 ~ wfh_post + factor(year_week),
data = tb,
index = c("personid", "year_week"),
model = "pooling"
)
stargazer(
out.pl, type = "text",
omit.stat = c("ser", "f"),
no.space = TRUE, header = FALSE,
title = "Pooled OLS results"
)
##
## Pooled OLS results
## ====================================================
## Dependent variable:
## ---------------------------
## perform1
## ----------------------------------------------------
## wfh_post 0.277***
## (0.030)
## factor(year_week)2022-02 1.986***
## (0.131)
## factor(year_week)2022-03 2.196***
## (0.132)
## factor(year_week)2022-04 2.360***
## (0.131)
## factor(year_week)2022-05 2.111***
## (0.130)
## factor(year_week)2022-06 2.082***
## (0.130)
## factor(year_week)2022-07 1.763***
## (0.130)
## factor(year_week)2022-08 1.990***
## (0.130)
## factor(year_week)2022-09 1.896***
## (0.130)
## factor(year_week)2022-10 1.979***
## (0.129)
## factor(year_week)2022-11 1.968***
## (0.129)
## factor(year_week)2022-12 2.111***
## (0.129)
## factor(year_week)2022-13 2.198***
## (0.128)
## factor(year_week)2022-14 2.185***
## (0.130)
## factor(year_week)2022-15 2.006***
## (0.130)
## factor(year_week)2022-16 2.310***
## (0.130)
## factor(year_week)2022-17 2.354***
## (0.130)
## factor(year_week)2022-18 2.229***
## (0.129)
## factor(year_week)2022-19 1.733***
## (0.127)
## factor(year_week)2022-20 2.044***
## (0.127)
## factor(year_week)2022-21 2.151***
## (0.127)
## factor(year_week)2022-22 1.963***
## (0.129)
## factor(year_week)2022-23 1.741***
## (0.128)
## factor(year_week)2022-24 2.269***
## (0.128)
## factor(year_week)2022-25 1.699***
## (0.128)
## factor(year_week)2022-26 1.781***
## (0.128)
## factor(year_week)2022-27 1.913***
## (0.128)
## factor(year_week)2022-28 2.070***
## (0.128)
## factor(year_week)2022-29 2.085***
## (0.128)
## factor(year_week)2022-30 1.977***
## (0.127)
## factor(year_week)2022-31 2.205***
## (0.127)
## factor(year_week)2022-32 1.961***
## (0.127)
## factor(year_week)2022-33 2.103***
## (0.127)
## factor(year_week)2022-34 1.940***
## (0.126)
## factor(year_week)2022-35 1.649***
## (0.126)
## factor(year_week)2022-36 1.664***
## (0.126)
## factor(year_week)2022-37 2.033***
## (0.127)
## factor(year_week)2022-38 2.294***
## (0.127)
## factor(year_week)2022-39 1.825***
## (0.127)
## factor(year_week)2022-40 2.094***
## (0.127)
## factor(year_week)2022-41 1.293***
## (0.126)
## factor(year_week)2022-42 1.929***
## (0.127)
## factor(year_week)2022-43 1.801***
## (0.127)
## factor(year_week)2022-44 1.561***
## (0.127)
## factor(year_week)2022-45 1.460***
## (0.127)
## factor(year_week)2022-46 1.349***
## (0.127)
## factor(year_week)2022-47 1.434***
## (0.126)
## factor(year_week)2022-48 1.510***
## (0.127)
## factor(year_week)2022-49 1.594***
## (0.127)
## factor(year_week)2022-50 1.614***
## (0.129)
## factor(year_week)2022-51 1.693***
## (0.129)
## factor(year_week)2022-52 1.538***
## (0.129)
## factor(year_week)2022-53 1.485***
## (0.129)
## factor(year_week)2023-01 1.621***
## (0.131)
## factor(year_week)2023-02 1.978***
## (0.130)
## factor(year_week)2023-03 1.826***
## (0.131)
## factor(year_week)2023-04 1.400***
## (0.132)
## factor(year_week)2023-05 1.106***
## (0.131)
## factor(year_week)2023-06 1.467***
## (0.132)
## factor(year_week)2023-07 1.531***
## (0.132)
## factor(year_week)2023-08 1.588***
## (0.131)
## factor(year_week)2023-09 1.673***
## (0.133)
## factor(year_week)2023-10 1.802***
## (0.132)
## factor(year_week)2023-11 1.766***
## (0.134)
## factor(year_week)2023-12 1.712***
## (0.134)
## factor(year_week)2023-13 1.814***
## (0.135)
## factor(year_week)2023-14 1.588***
## (0.135)
## factor(year_week)2023-15 2.020***
## (0.134)
## factor(year_week)2023-16 1.957***
## (0.135)
## factor(year_week)2023-17 2.091***
## (0.137)
## factor(year_week)2023-18 1.629***
## (0.138)
## factor(year_week)2023-19 1.679***
## (0.136)
## factor(year_week)2023-20 1.928***
## (0.137)
## factor(year_week)2023-21 1.647***
## (0.135)
## factor(year_week)2023-22 1.762***
## (0.136)
## factor(year_week)2023-23 1.621***
## (0.136)
## factor(year_week)2023-24 1.815***
## (0.136)
## factor(year_week)2023-25 1.644***
## (0.137)
## factor(year_week)2023-26 1.876***
## (0.137)
## factor(year_week)2023-27 1.981***
## (0.138)
## factor(year_week)2023-28 2.156***
## (0.139)
## factor(year_week)2023-29 2.049***
## (0.139)
## factor(year_week)2023-30 2.037***
## (0.138)
## factor(year_week)2023-31 2.170***
## (0.138)
## factor(year_week)2023-32 2.205***
## (0.140)
## factor(year_week)2023-33 2.099***
## (0.139)
## Constant -1.918***
## (0.096)
## ----------------------------------------------------
## Observations 9,847
## R2 0.112
## Adjusted R2 0.104
## ====================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
First Differences
# First Differences (without intercept)
out.fd <- plm(
perform1 ~ 0 + wfh_post + factor(year_week),
data = tb,
index = c("personid", "year_week"),
model = "fd"
)
stargazer(
out.fd, type = "text",
omit.stat = c("ser", "f"),
no.space = TRUE, header = FALSE,
title = "First-Differences (without intercept) results"
)
##
## First-Differences (without intercept) results
## ====================================================
## Dependent variable:
## ---------------------------
## perform1
## ----------------------------------------------------
## wfh_post 0.267*
## (0.155)
## factor(year_week)2022-01 -0.959
## (0.683)
## factor(year_week)2022-02 1.061
## (0.676)
## factor(year_week)2022-03 1.259*
## (0.671)
## factor(year_week)2022-04 1.444**
## (0.666)
## factor(year_week)2022-05 1.305**
## (0.661)
## factor(year_week)2022-06 1.281*
## (0.656)
## factor(year_week)2022-07 0.956
## (0.651)
## factor(year_week)2022-08 1.246*
## (0.646)
## factor(year_week)2022-09 1.132*
## (0.641)
## factor(year_week)2022-10 1.241*
## (0.635)
## factor(year_week)2022-11 1.250**
## (0.630)
## factor(year_week)2022-12 1.459**
## (0.624)
## factor(year_week)2022-13 1.548**
## (0.619)
## factor(year_week)2022-14 1.477**
## (0.616)
## factor(year_week)2022-15 1.287**
## (0.614)
## factor(year_week)2022-16 1.620***
## (0.610)
## factor(year_week)2022-17 1.716***
## (0.606)
## factor(year_week)2022-18 1.652***
## (0.602)
## factor(year_week)2022-19 1.270**
## (0.597)
## factor(year_week)2022-20 1.604***
## (0.592)
## factor(year_week)2022-21 1.722***
## (0.587)
## factor(year_week)2022-22 1.429**
## (0.586)
## factor(year_week)2022-23 1.088*
## (0.585)
## factor(year_week)2022-24 1.493**
## (0.584)
## factor(year_week)2022-25 0.794
## (0.582)
## factor(year_week)2022-26 0.786
## (0.579)
## factor(year_week)2022-27 0.817
## (0.576)
## factor(year_week)2022-28 0.848
## (0.573)
## factor(year_week)2022-29 0.782
## (0.570)
## factor(year_week)2022-30 0.663
## (0.566)
## factor(year_week)2022-31 0.819
## (0.562)
## factor(year_week)2022-32 0.511
## (0.558)
## factor(year_week)2022-33 0.574
## (0.553)
## factor(year_week)2022-34 0.445
## (0.548)
## factor(year_week)2022-35 0.173
## (0.542)
## factor(year_week)2022-36 0.185
## (0.537)
## factor(year_week)2022-37 0.519
## (0.533)
## factor(year_week)2022-38 0.773
## (0.528)
## factor(year_week)2022-39 0.302
## (0.523)
## factor(year_week)2022-40 0.584
## (0.518)
## factor(year_week)2022-41 -0.272
## (0.513)
## factor(year_week)2022-42 0.372
## (0.508)
## factor(year_week)2022-43 0.240
## (0.502)
## factor(year_week)2022-44 0.046
## (0.497)
## factor(year_week)2022-45 -0.077
## (0.492)
## factor(year_week)2022-46 -0.220
## (0.488)
## factor(year_week)2022-47 -0.153
## (0.483)
## factor(year_week)2022-48 -0.143
## (0.480)
## factor(year_week)2022-49 -0.085
## (0.477)
## factor(year_week)2022-50 -0.108
## (0.470)
## factor(year_week)2022-51 -0.066
## (0.466)
## factor(year_week)2022-52 -0.206
## (0.462)
## factor(year_week)2022-53 -0.254
## (0.458)
## factor(year_week)2023-01 -0.160
## (0.455)
## factor(year_week)2023-02 0.218
## (0.450)
## factor(year_week)2023-03 0.126
## (0.446)
## factor(year_week)2023-04 -0.339
## (0.442)
## factor(year_week)2023-05 -0.610
## (0.437)
## factor(year_week)2023-06 -0.273
## (0.431)
## factor(year_week)2023-07 -0.228
## (0.425)
## factor(year_week)2023-08 -0.181
## (0.419)
## factor(year_week)2023-09 -0.131
## (0.413)
## factor(year_week)2023-10 0.035
## (0.406)
## factor(year_week)2023-11 -0.053
## (0.399)
## factor(year_week)2023-12 -0.107
## (0.392)
## factor(year_week)2023-13 -0.035
## (0.385)
## factor(year_week)2023-14 -0.294
## (0.377)
## factor(year_week)2023-15 0.158
## (0.368)
## factor(year_week)2023-16 0.073
## (0.358)
## factor(year_week)2023-17 0.208
## (0.350)
## factor(year_week)2023-18 -0.351
## (0.341)
## factor(year_week)2023-19 -0.232
## (0.330)
## factor(year_week)2023-20 0.005
## (0.320)
## factor(year_week)2023-21 -0.274
## (0.308)
## factor(year_week)2023-22 -0.179
## (0.296)
## factor(year_week)2023-23 -0.292
## (0.284)
## factor(year_week)2023-24 -0.139
## (0.270)
## factor(year_week)2023-25 -0.313
## (0.255)
## factor(year_week)2023-26 -0.102
## (0.239)
## factor(year_week)2023-27 -0.037
## (0.223)
## factor(year_week)2023-28 0.135
## (0.206)
## factor(year_week)2023-29 0.038
## (0.186)
## factor(year_week)2023-30 0.075
## (0.162)
## factor(year_week)2023-31 0.160
## (0.133)
## factor(year_week)2023-32 0.079
## (0.095)
## ----------------------------------------------------
## Observations 9,712
## R2 0.116
## Adjusted R2 0.108
## ====================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
Fixed Effects
# Fixed Effects (within)
out.fe <- plm(
perform1 ~ 0 + wfh_post + factor(year_week),
data = tb,
index = c("personid", "year_week"),
model = "within"
)
stargazer(
out.fe, type = "text",
omit.stat = c("ser", "f"),
no.space = TRUE, header = FALSE,
title = "Fixed effects results"
)
##
## Fixed effects results
## ====================================================
## Dependent variable:
## ---------------------------
## perform1
## ----------------------------------------------------
## wfh_post 0.295***
## (0.033)
## factor(year_week)2022-01 -2.094***
## (0.117)
## factor(year_week)2022-02 -0.131
## (0.113)
## factor(year_week)2022-03 0.073
## (0.113)
## factor(year_week)2022-04 0.243**
## (0.113)
## factor(year_week)2022-05 0.016
## (0.112)
## factor(year_week)2022-06 -0.013
## (0.112)
## factor(year_week)2022-07 -0.329***
## (0.112)
## factor(year_week)2022-08 -0.107
## (0.112)
## factor(year_week)2022-09 -0.201*
## (0.112)
## factor(year_week)2022-10 -0.119
## (0.112)
## factor(year_week)2022-11 -0.133
## (0.111)
## factor(year_week)2022-12 0.033
## (0.111)
## factor(year_week)2022-13 0.121
## (0.111)
## factor(year_week)2022-14 0.132
## (0.112)
## factor(year_week)2022-15 -0.050
## (0.112)
## factor(year_week)2022-16 0.254**
## (0.112)
## factor(year_week)2022-17 0.305***
## (0.112)
## factor(year_week)2022-18 0.178
## (0.112)
## factor(year_week)2022-19 -0.321***
## (0.110)
## factor(year_week)2022-20 -0.005
## (0.110)
## factor(year_week)2022-21 0.102
## (0.110)
## factor(year_week)2022-22 -0.065
## (0.111)
## factor(year_week)2022-23 -0.269**
## (0.111)
## factor(year_week)2022-24 0.265**
## (0.111)
## factor(year_week)2022-25 -0.305***
## (0.111)
## factor(year_week)2022-26 -0.210*
## (0.110)
## factor(year_week)2022-27 -0.096
## (0.110)
## factor(year_week)2022-28 0.077
## (0.111)
## factor(year_week)2022-29 0.076
## (0.110)
## factor(year_week)2022-30 -0.030
## (0.109)
## factor(year_week)2022-31 0.205*
## (0.110)
## factor(year_week)2022-32 -0.044
## (0.110)
## factor(year_week)2022-33 0.093
## (0.110)
## factor(year_week)2022-34 -0.073
## (0.109)
## factor(year_week)2022-35 -0.364***
## (0.109)
## factor(year_week)2022-36 -0.350***
## (0.109)
## factor(year_week)2022-37 -0.011
## (0.110)
## factor(year_week)2022-38 0.254**
## (0.110)
## factor(year_week)2022-39 -0.214*
## (0.110)
## factor(year_week)2022-40 0.076
## (0.110)
## factor(year_week)2022-41 -0.729***
## (0.109)
## factor(year_week)2022-42 -0.093
## (0.109)
## factor(year_week)2022-43 -0.225**
## (0.109)
## factor(year_week)2022-44 -0.440***
## (0.109)
## factor(year_week)2022-45 -0.557***
## (0.110)
## factor(year_week)2022-46 -0.671***
## (0.110)
## factor(year_week)2022-47 -0.584***
## (0.109)
## factor(year_week)2022-48 -0.530***
## (0.110)
## factor(year_week)2022-49 -0.442***
## (0.109)
## factor(year_week)2022-50 -0.441***
## (0.109)
## factor(year_week)2022-51 -0.359***
## (0.108)
## factor(year_week)2022-52 -0.496***
## (0.108)
## factor(year_week)2022-53 -0.572***
## (0.109)
## factor(year_week)2023-01 -0.441***
## (0.110)
## factor(year_week)2023-02 -0.094
## (0.110)
## factor(year_week)2023-03 -0.222**
## (0.110)
## factor(year_week)2023-04 -0.654***
## (0.111)
## factor(year_week)2023-05 -0.933***
## (0.110)
## factor(year_week)2023-06 -0.593***
## (0.111)
## factor(year_week)2023-07 -0.536***
## (0.111)
## factor(year_week)2023-08 -0.481***
## (0.110)
## factor(year_week)2023-09 -0.405***
## (0.112)
## factor(year_week)2023-10 -0.266**
## (0.111)
## factor(year_week)2023-11 -0.330***
## (0.113)
## factor(year_week)2023-12 -0.385***
## (0.113)
## factor(year_week)2023-13 -0.285**
## (0.113)
## factor(year_week)2023-14 -0.502***
## (0.114)
## factor(year_week)2023-15 -0.070
## (0.113)
## factor(year_week)2023-16 -0.134
## (0.113)
## factor(year_week)2023-17 -0.012
## (0.115)
## factor(year_week)2023-18 -0.501***
## (0.116)
## factor(year_week)2023-19 -0.434***
## (0.114)
## factor(year_week)2023-20 -0.192*
## (0.115)
## factor(year_week)2023-21 -0.449***
## (0.114)
## factor(year_week)2023-22 -0.346***
## (0.114)
## factor(year_week)2023-23 -0.484***
## (0.114)
## factor(year_week)2023-24 -0.279**
## (0.115)
## factor(year_week)2023-25 -0.451***
## (0.115)
## factor(year_week)2023-26 -0.212*
## (0.115)
## factor(year_week)2023-27 -0.123
## (0.116)
## factor(year_week)2023-28 0.049
## (0.117)
## factor(year_week)2023-29 -0.044
## (0.116)
## factor(year_week)2023-30 -0.055
## (0.116)
## factor(year_week)2023-31 0.080
## (0.116)
## factor(year_week)2023-32 0.095
## (0.117)
## ----------------------------------------------------
## Observations 9,847
## R2 0.146
## Adjusted R2 0.126
## ====================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
Pooled OLS with Person Dummies
# Pooled OLS with person dummies
out.pool <- plm(
perform1 ~ wfh_post + factor(year_week) + factor(personid),
data = tb,
index = c("personid", "year_week"),
model = "pooling"
)
stargazer(
out.pool, type = "text",
omit.stat = c("ser", "f"),
omit = c(".*personid.*"),
omit.labels = c("Person Dummies"),
no.space = TRUE, header = FALSE,
title = "Pooled OLS (with person dummies) results"
)
##
## Pooled OLS (with person dummies) results
## ====================================================
## Dependent variable:
## ---------------------------
## perform1
## ----------------------------------------------------
## wfh_post 0.295***
## (0.033)
## factor(year_week)2022-02 1.963***
## (0.110)
## factor(year_week)2022-03 2.168***
## (0.110)
## factor(year_week)2022-04 2.337***
## (0.110)
## factor(year_week)2022-05 2.110***
## (0.109)
## factor(year_week)2022-06 2.081***
## (0.109)
## factor(year_week)2022-07 1.765***
## (0.109)
## factor(year_week)2022-08 1.988***
## (0.109)
## factor(year_week)2022-09 1.894***
## (0.109)
## factor(year_week)2022-10 1.976***
## (0.108)
## factor(year_week)2022-11 1.961***
## (0.108)
## factor(year_week)2022-12 2.128***
## (0.108)
## factor(year_week)2022-13 2.215***
## (0.108)
## factor(year_week)2022-14 2.227***
## (0.109)
## factor(year_week)2022-15 2.045***
## (0.109)
## factor(year_week)2022-16 2.349***
## (0.109)
## factor(year_week)2022-17 2.399***
## (0.109)
## factor(year_week)2022-18 2.273***
## (0.108)
## factor(year_week)2022-19 1.774***
## (0.107)
## factor(year_week)2022-20 2.089***
## (0.107)
## factor(year_week)2022-21 2.196***
## (0.107)
## factor(year_week)2022-22 2.029***
## (0.108)
## factor(year_week)2022-23 1.826***
## (0.108)
## factor(year_week)2022-24 2.359***
## (0.107)
## factor(year_week)2022-25 1.789***
## (0.107)
## factor(year_week)2022-26 1.884***
## (0.107)
## factor(year_week)2022-27 1.999***
## (0.107)
## factor(year_week)2022-28 2.171***
## (0.107)
## factor(year_week)2022-29 2.170***
## (0.107)
## factor(year_week)2022-30 2.065***
## (0.106)
## factor(year_week)2022-31 2.299***
## (0.107)
## factor(year_week)2022-32 2.051***
## (0.107)
## factor(year_week)2022-33 2.188***
## (0.107)
## factor(year_week)2022-34 2.022***
## (0.106)
## factor(year_week)2022-35 1.730***
## (0.105)
## factor(year_week)2022-36 1.745***
## (0.106)
## factor(year_week)2022-37 2.084***
## (0.106)
## factor(year_week)2022-38 2.349***
## (0.106)
## factor(year_week)2022-39 1.880***
## (0.107)
## factor(year_week)2022-40 2.170***
## (0.106)
## factor(year_week)2022-41 1.366***
## (0.106)
## factor(year_week)2022-42 2.001***
## (0.106)
## factor(year_week)2022-43 1.869***
## (0.106)
## factor(year_week)2022-44 1.654***
## (0.106)
## factor(year_week)2022-45 1.538***
## (0.107)
## factor(year_week)2022-46 1.424***
## (0.106)
## factor(year_week)2022-47 1.510***
## (0.106)
## factor(year_week)2022-48 1.564***
## (0.106)
## factor(year_week)2022-49 1.652***
## (0.106)
## factor(year_week)2022-50 1.654***
## (0.108)
## factor(year_week)2022-51 1.736***
## (0.108)
## factor(year_week)2022-52 1.599***
## (0.108)
## factor(year_week)2022-53 1.523***
## (0.109)
## factor(year_week)2023-01 1.654***
## (0.110)
## factor(year_week)2023-02 2.001***
## (0.110)
## factor(year_week)2023-03 1.872***
## (0.110)
## factor(year_week)2023-04 1.440***
## (0.111)
## factor(year_week)2023-05 1.162***
## (0.110)
## factor(year_week)2023-06 1.501***
## (0.111)
## factor(year_week)2023-07 1.559***
## (0.111)
## factor(year_week)2023-08 1.614***
## (0.111)
## factor(year_week)2023-09 1.689***
## (0.112)
## factor(year_week)2023-10 1.828***
## (0.111)
## factor(year_week)2023-11 1.764***
## (0.113)
## factor(year_week)2023-12 1.709***
## (0.113)
## factor(year_week)2023-13 1.810***
## (0.114)
## factor(year_week)2023-14 1.593***
## (0.114)
## factor(year_week)2023-15 2.025***
## (0.113)
## factor(year_week)2023-16 1.961***
## (0.114)
## factor(year_week)2023-17 2.083***
## (0.116)
## factor(year_week)2023-18 1.593***
## (0.116)
## factor(year_week)2023-19 1.661***
## (0.114)
## factor(year_week)2023-20 1.903***
## (0.116)
## factor(year_week)2023-21 1.645***
## (0.114)
## factor(year_week)2023-22 1.748***
## (0.115)
## factor(year_week)2023-23 1.611***
## (0.115)
## factor(year_week)2023-24 1.815***
## (0.115)
## factor(year_week)2023-25 1.644***
## (0.115)
## factor(year_week)2023-26 1.883***
## (0.115)
## factor(year_week)2023-27 1.972***
## (0.116)
## factor(year_week)2023-28 2.144***
## (0.117)
## factor(year_week)2023-29 2.050***
## (0.117)
## factor(year_week)2023-30 2.040***
## (0.116)
## factor(year_week)2023-31 2.174***
## (0.116)
## factor(year_week)2023-32 2.189***
## (0.118)
## factor(year_week)2023-33 2.094***
## (0.117)
## Constant -1.692***
## (0.117)
## ----------------------------------------------------
## Person Dummies Yes
## ----------------------------------------------------
## Observations 9,847
## R2 0.386
## Adjusted R2 0.372
## ====================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
to interpret our models we will use the summary table we have seen in the lab8.
# Summary table
stargazer(
out.pool, out.pl, out.fd, out.fe,
type = "text",
column.labels = c("Pool with dummies", "Pool no dummies", "FD", "FE"),
omit.stat = c("ser", "f"),
omit = c("factor\\(year_week\\).*", ".*personid.*"),
omit.labels = c("Time Dummies", "Person Dummies"),
model.names = FALSE,
no.space = TRUE, header = FALSE,
title = "Summary results"
)
##
## Summary results
## =================================================================
## Dependent variable:
## --------------------------------------------------
## perform1
## Pool with dummies Pool no dummies FD FE
## (1) (2) (3) (4)
## -----------------------------------------------------------------
## wfh_post 0.295*** 0.277*** 0.267* 0.295***
## (0.033) (0.030) (0.155) (0.033)
## Constant -1.692*** -1.918***
## (0.117) (0.096)
## -----------------------------------------------------------------
## Time Dummies Yes Yes Yes Yes
## Person Dummies Yes No No No
## -----------------------------------------------------------------
## Observations 9,847 9,847 9,712 9,847
## R2 0.386 0.112 0.116 0.146
## Adjusted R2 0.372 0.104 0.108 0.126
## =================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
The panel regression estimates the impact of the building-issue-induced WFH shock using a Difference-in-Differences (DiD) setup, where the key variable is wfh_post = treated × post. The dependent variable perform1 is an overall performance z-score, so coefficients can be interpreted in standard deviation units. Across specifications, the estimated effect of wfh_post is consistently positive and statistically significant: 0.295* in the pooled model with person dummies, 0.277* in pooled OLS without person dummies, 0.267* in first differences, and 0.295* in the fixed-effects model. This stability strongly suggests that the result is not driven by modeling choice but reflects a robust pattern in the data. Substantively, the preferred FE estimate (~0.295) implies that employees affected by the building issues experienced an increase of about 0.30 standard deviations in performance after the event, relative to unaffected employees and relative to the pre-period. Interpreted within the DiD logic, this is the additional post-event shift for the treated group beyond general time trends and time-invariant individual differences. The consistency across Pool/FD/FE strengthens the credibility of the estimated effect, assuming the parallel trends assumption is reasonably satisfied.
Breusch–Godfrey/Wooldridge test for serial correlation in FE residuals
pbgtest(out.fe)
##
## Breusch-Godfrey/Wooldridge test for serial correlation in panel models
##
## data: perform1 ~ 0 + wfh_post + factor(year_week)
## chisq = 1067.3, df = 27, p-value < 2.2e-16
## alternative hypothesis: serial correlation in idiosyncratic errors
The Breusch–Godfrey/Wooldridge test strongly rejects the null hypothesis of no serial correlation (p-value < 2.2e-16). This indicates substantial serial correlation in the weekly panel residuals of the FE specification. In practical terms, this does not invalidate the estimated coefficient itself, but it does mean that conventional (non-robust) standard errors are likely biased downward, which can overstate statistical significance.
Therefore, inference should rely on robust or cluster-robust standard errors, ideally clustered at the person level for this weekly employee panel.
Because of that the Robust standard errors are calculated next.
Robust Standard Errors for FE model
# Robust SEs (Arellano)
robust_se <- sqrt(diag(plm::vcovHC(out.fe, method = "arellano")))
stargazer(
out.fe, out.fe,
se = list(NULL, robust_se),
column.labels = c("Non-Robust SEs", "Robust SEs"),
type = "text",
omit.stat = c("ser", "f"),
omit = c("factor\\(year_week\\).*"),
omit.labels = c("Time Dummies"),
model.names = FALSE,
dep.var.labels.include = FALSE,
no.space = TRUE, header = FALSE,
title = "Serial correlation results"
)
##
## Serial correlation results
## =========================================
## Dependent variable:
## ----------------------------
## Non-Robust SEs Robust SEs
## (1) (2)
## -----------------------------------------
## wfh_post 0.295*** 0.295***
## (0.033) (0.059)
## -----------------------------------------
## Time Dummies Yes Yes
## -----------------------------------------
## Observations 9,847 9,847
## R2 0.146 0.146
## Adjusted R2 0.126 0.126
## =========================================
## Note: *p<0.1; **p<0.05; ***p<0.01
As already mentioned, the estimated DiD effect of the building-issue-induced WFH shock on overall performance is positive and economically meaningful. The FE coefficient on wfh_post is approximately 0.295, implying an increase of about 0.30 standard deviations in the performance z-score for affected employees in the post period relative to unaffected employees.
Given evidence of serial correlation, we report Arellano-robust standard errors. The robust correction increases the standard error from 0.033 to 0.059, but the estimated effect remains statistically significant, indicating that the main result is robust to serial-correlation-consistent inference.
Average performance for promotion for male and female
final_all %>%
group_by(gender) %>%
summarise(Average_Performance = mean(mean_overall_perf_z_score)) %>%
arrange(desc(Average_Performance))
## # A tibble: 2 × 2
## gender Average_Performance
## <fct> <dbl>
## 1 Female 0.154
## 2 Male -0.280
Average performance when promoted for male and female
final_all %>%
filter(promote_switch == 1) %>%
group_by(gender) %>%
summarise(Average_Performance_Promoted = mean(mean_overall_perf_z_score)) %>%
arrange(desc(Average_Performance_Promoted))
## # A tibble: 2 × 2
## gender Average_Performance_Promoted
## <fct> <dbl>
## 1 Female 0.398
## 2 Male -0.0283
Promotion rate by gender
final_all %>%
group_by(gender) %>%
summarise(Promotion_Rate = mean(promote_switch)) %>%
arrange(desc(Promotion_Rate)) %>%
ggplot(aes(x = gender, y = Promotion_Rate, fill = gender)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = c("Male" = "skyblue", "Female" = "#F1948D")) +
geom_text(aes(label = scales::percent(Promotion_Rate, accuracy = 0.1)), vjust = -0.5) +
labs(title = "Promotion Rate by Gender", x = "Gender", y = "Promotion Rate") +
theme_minimal()
Promotionrate by performance quartile
final_all <- final_all %>%
mutate(performance_quartile = ntile(mean_overall_perf_z_score, 4))
final_all %>%
group_by(performance_quartile, gender) %>%
summarise(Promotion_Rate = mean(promote_switch)) %>%
arrange(performance_quartile, desc(Promotion_Rate)) %>%
ggplot(aes(x = factor(performance_quartile), y = Promotion_Rate, fill = gender)) +
geom_bar(stat = "identity", position = "dodge") +
scale_fill_manual(values = c("Male" = "skyblue", "Female" = "#F1948D")) +
geom_text(aes(label = scales::percent(Promotion_Rate, accuracy = 0.1)),
position = position_dodge(width = 0.9), vjust = -0.5) +
labs(title = "Promotion Rate by Performance Quartile and Gender",
x = "Performance Quartile",
y = "Promotion Rate") +
theme_minimal()
## `summarise()` has grouped output by 'performance_quartile'. You can override
## using the `.groups` argument.
Average performance over months for male, female and overall
final_panel_weekly %>%
group_by(month, gender) %>%
summarise(Average_Performance = mean(perform1, na.rm = TRUE)) %>%
ungroup() %>%
group_by(month) %>%
summarise(Overall_Average_Performance = mean(Average_Performance, na.rm = TRUE)) %>%
left_join(final_panel_weekly %>%
group_by(month, gender) %>%
summarise(Average_Performance = mean(perform1, na.rm = TRUE)), by = "month") %>%
ggplot(aes(x = month)) +
geom_line(aes(y = Average_Performance, color = gender), size = 1) +
geom_line(aes(y = Overall_Average_Performance, color = "Overall"), size = 1, linetype = "dashed") +
scale_color_manual(values = c("Male" = "skyblue", "Female" = "#F1948D", "Overall" = "black")) +
labs(title = "Average Performance Over Time by Gender with Overall Performance",
x = "Week",
y = "Average Performance (Z-Score)") +
theme_minimal() +
theme(legend.title = element_blank()) +
scale_y_continuous(limits = c(-1, 1))
## `summarise()` has grouped output by 'month'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'month'. You can override using the
## `.groups` argument.
……